#randomforest — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #randomforest, aggregated by home.social.
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DATE: May 27, 2026 at 10:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Machine learning uncovers how childhood trauma amplifies genetic risks for depression
Depression is a highly common mental health condition that affects millions of people worldwide. Medical professionals have established that the disorder arises from a combination of biological vulnerabilities and external stressors. A recent study published in the Proceedings of the National Academy of Sciences utilized a machine learning approach to map thousands of instances where early traumatic events amplify genetic risks for depression. The researchers discovered that childhood trauma exerts a profound influence on genetic susceptibility, highlighting biological connections that conventional statistical methods have routinely missed.
Scientists recognize that individual variations in human DNA do not entirely determine who will develop major depression. A person might carry specific genetic risk factors but never experience depressive symptoms unless they encounter severe environmental stress. This concept is often referred to as a gene by environment interaction. Researchers have struggled to identify these specific interactions because the genetic risks for depression are spread across hundreds of different locations within the human genome.
When scientists attempt to look for these interactions, they typically employ genome wide interaction studies. This standard method tests one genetic variant and one environmental factor at a time to see if they collectively influence a disease. Unfortunately, this one by one approach typically lacks the statistical power required to find subtle, nonlinear patterns scattered across so much genetic data. The sheer volume of tests creates statistical noise that obscures the true results.
Yue Hua, a biostatistician at the Yale University School of Public Health, led a research team to look past the limitations of the traditional approach. Hua and coauthors Jeffrey R. Gruen and Heping Zhang decided to analyze massive datasets using an advanced machine learning technique. They reasoned that algorithms could look at the data more holistically than standard linear equations.
The researchers turned to the UK Biobank, a large database containing genetic and health information from volunteers in the United Kingdom. After filtering the data for completeness and matching cases, they established a study group of 38,018 participants. Half of these individuals had a diagnosis of depression, and the other half served as a control group with no reported mental illness.
To measure environmental stress, the team used participant questionnaire responses regarding past traumatic experiences. They divided these experiences into three distinct categories. The categories included childhood trauma, adult trauma, and catastrophic trauma.
The genetic data consisted of over 285,000 single nucleotide polymorphisms. A single nucleotide polymorphism is a tiny, naturally occurring variation involving just one letter in a person’s DNA sequence. To search for connections between these genetic variations and the reported trauma, the team used an algorithm known as a random forest.
A random forest model operates by building hundreds of separate decision trees using random subsets of the data. Each tree attempts to predict whether an individual has depression by splitting the data according to genetic variants and trauma types. If a specific genetic variant and a specific type of trauma consistently end up next to each other on these decision pathways, the algorithm flags them as an interacting pair.
When the researchers ran a traditional genome wide interaction study on the data, the results were predictably flat. They found zero variations that met the threshold to be considered anything other than not statistically significant. This failure aligned with previous research efforts that struggled to find robust genetic interaction signals.
Applying the random forest method yielded a vastly different outcome. The algorithm identified 8,225 specific pairs where a genetic variation and a trauma exposure appeared to work together to increase depression risk. These variations mapped back to 1,732 unique genes across the human genome.
When classifying the results by trauma category, early life adversity stood out prominently. Childhood trauma was involved in the largest proportion of the identified genetic interactions. This suggests that trauma experienced during early developmental years plays a particularly potent role in unlocking genetic vulnerabilities.
To verify this pattern mathematically, the team calculated the heritability of depression for different subgroups in their study. Heritability is a statistical estimate of how much a trait is determined by genetic factors as opposed to environmental factors within a specific population.
For the individuals who reported experiencing childhood trauma, the estimated heritability of depression reached 13.3 percent. By comparison, the heritability estimate dropped to 6.0 percent for individuals who had not been exposed to childhood trauma. This difference mathematically demonstrates that genetic factors exert much more influence on depression when early life stress is present.
Adult and catastrophic trauma also showed mildly elevated heritability patterns compared to unexposed individuals. However, the differences for those remaining trauma categories were not statistically significant.
The researchers then focused on 22 top genes that showed the highest number of interactions with trauma in their machine learning model. A review of existing medical literature revealed that nearly all of these genes have been previously linked to psychiatric or neurological conditions. Some of the flagged genes are associated with bipolar disorder, memory function, and sleep disturbances.
To confirm that their algorithm was detecting real biological phenomena, the team tested their top findings in a completely different group of people. They accessed data from the Adolescent Brain Cognitive Development study, which tracks the health and development of children in the United States. Given that the participants were children aged nine and ten, the researchers focused exclusively on validating the childhood trauma interactions.
By running specialized genetic analyses on this separate cohort, the researchers replicated the interaction signals for 13 of the 22 top genes. Finding similar biological patterns in an independent group of American children provided secondary validation for the patterns originally detected in selectively older adult cohorts.
While the findings offer an expansive new view of depression, the researchers noted several limitations to their methodology. During the initial data sorting phase, hundreds of thousands of participants had to be removed from consideration because they skipped questions on the trauma surveys. This massive exclusion drastically reduced the sample size and could introduce unknown biases into the study population.
Another limitation rests within the mechanics of the random forest algorithm itself. The decision tree structure naturally favors variables that have very strong independent effects on an outcome. As a result, the algorithm might occasionally flag a gene and a trauma type as an interacting pair when they actually just have very strong, entirely separate impacts on depression.
Future scientific work will need to resolve these algorithmic gray areas. Researchers hope to use these machine learning findings as a screening step, which can then be followed by biological laboratory tests to verify exactly how a gene functions under stress. Identifying the exact cellular mechanisms could eventually open new pathways for treating stress related psychiatric conditions.
The study, “Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study,” was authored by Yue Hua, Jeffrey R. Gruen, and Heping Zhang.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DepressionGenetics #ChildhoodTrauma #GeneEnvironmentInteraction #MachineLearningInMedicine #RandomForest #UKBiobank #AdolescentBrainCognitiveDevelopment #Heritability # PsychiatricGenomics #TraumaAndBiology
-
DATE: May 27, 2026 at 10:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Machine learning uncovers how childhood trauma amplifies genetic risks for depression
Depression is a highly common mental health condition that affects millions of people worldwide. Medical professionals have established that the disorder arises from a combination of biological vulnerabilities and external stressors. A recent study published in the Proceedings of the National Academy of Sciences utilized a machine learning approach to map thousands of instances where early traumatic events amplify genetic risks for depression. The researchers discovered that childhood trauma exerts a profound influence on genetic susceptibility, highlighting biological connections that conventional statistical methods have routinely missed.
Scientists recognize that individual variations in human DNA do not entirely determine who will develop major depression. A person might carry specific genetic risk factors but never experience depressive symptoms unless they encounter severe environmental stress. This concept is often referred to as a gene by environment interaction. Researchers have struggled to identify these specific interactions because the genetic risks for depression are spread across hundreds of different locations within the human genome.
When scientists attempt to look for these interactions, they typically employ genome wide interaction studies. This standard method tests one genetic variant and one environmental factor at a time to see if they collectively influence a disease. Unfortunately, this one by one approach typically lacks the statistical power required to find subtle, nonlinear patterns scattered across so much genetic data. The sheer volume of tests creates statistical noise that obscures the true results.
Yue Hua, a biostatistician at the Yale University School of Public Health, led a research team to look past the limitations of the traditional approach. Hua and coauthors Jeffrey R. Gruen and Heping Zhang decided to analyze massive datasets using an advanced machine learning technique. They reasoned that algorithms could look at the data more holistically than standard linear equations.
The researchers turned to the UK Biobank, a large database containing genetic and health information from volunteers in the United Kingdom. After filtering the data for completeness and matching cases, they established a study group of 38,018 participants. Half of these individuals had a diagnosis of depression, and the other half served as a control group with no reported mental illness.
To measure environmental stress, the team used participant questionnaire responses regarding past traumatic experiences. They divided these experiences into three distinct categories. The categories included childhood trauma, adult trauma, and catastrophic trauma.
The genetic data consisted of over 285,000 single nucleotide polymorphisms. A single nucleotide polymorphism is a tiny, naturally occurring variation involving just one letter in a person’s DNA sequence. To search for connections between these genetic variations and the reported trauma, the team used an algorithm known as a random forest.
A random forest model operates by building hundreds of separate decision trees using random subsets of the data. Each tree attempts to predict whether an individual has depression by splitting the data according to genetic variants and trauma types. If a specific genetic variant and a specific type of trauma consistently end up next to each other on these decision pathways, the algorithm flags them as an interacting pair.
When the researchers ran a traditional genome wide interaction study on the data, the results were predictably flat. They found zero variations that met the threshold to be considered anything other than not statistically significant. This failure aligned with previous research efforts that struggled to find robust genetic interaction signals.
Applying the random forest method yielded a vastly different outcome. The algorithm identified 8,225 specific pairs where a genetic variation and a trauma exposure appeared to work together to increase depression risk. These variations mapped back to 1,732 unique genes across the human genome.
When classifying the results by trauma category, early life adversity stood out prominently. Childhood trauma was involved in the largest proportion of the identified genetic interactions. This suggests that trauma experienced during early developmental years plays a particularly potent role in unlocking genetic vulnerabilities.
To verify this pattern mathematically, the team calculated the heritability of depression for different subgroups in their study. Heritability is a statistical estimate of how much a trait is determined by genetic factors as opposed to environmental factors within a specific population.
For the individuals who reported experiencing childhood trauma, the estimated heritability of depression reached 13.3 percent. By comparison, the heritability estimate dropped to 6.0 percent for individuals who had not been exposed to childhood trauma. This difference mathematically demonstrates that genetic factors exert much more influence on depression when early life stress is present.
Adult and catastrophic trauma also showed mildly elevated heritability patterns compared to unexposed individuals. However, the differences for those remaining trauma categories were not statistically significant.
The researchers then focused on 22 top genes that showed the highest number of interactions with trauma in their machine learning model. A review of existing medical literature revealed that nearly all of these genes have been previously linked to psychiatric or neurological conditions. Some of the flagged genes are associated with bipolar disorder, memory function, and sleep disturbances.
To confirm that their algorithm was detecting real biological phenomena, the team tested their top findings in a completely different group of people. They accessed data from the Adolescent Brain Cognitive Development study, which tracks the health and development of children in the United States. Given that the participants were children aged nine and ten, the researchers focused exclusively on validating the childhood trauma interactions.
By running specialized genetic analyses on this separate cohort, the researchers replicated the interaction signals for 13 of the 22 top genes. Finding similar biological patterns in an independent group of American children provided secondary validation for the patterns originally detected in selectively older adult cohorts.
While the findings offer an expansive new view of depression, the researchers noted several limitations to their methodology. During the initial data sorting phase, hundreds of thousands of participants had to be removed from consideration because they skipped questions on the trauma surveys. This massive exclusion drastically reduced the sample size and could introduce unknown biases into the study population.
Another limitation rests within the mechanics of the random forest algorithm itself. The decision tree structure naturally favors variables that have very strong independent effects on an outcome. As a result, the algorithm might occasionally flag a gene and a trauma type as an interacting pair when they actually just have very strong, entirely separate impacts on depression.
Future scientific work will need to resolve these algorithmic gray areas. Researchers hope to use these machine learning findings as a screening step, which can then be followed by biological laboratory tests to verify exactly how a gene functions under stress. Identifying the exact cellular mechanisms could eventually open new pathways for treating stress related psychiatric conditions.
The study, “Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study,” was authored by Yue Hua, Jeffrey R. Gruen, and Heping Zhang.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DepressionGenetics #ChildhoodTrauma #GeneEnvironmentInteraction #MachineLearningInMedicine #RandomForest #UKBiobank #AdolescentBrainCognitiveDevelopment #Heritability # PsychiatricGenomics #TraumaAndBiology
-
DATE: May 27, 2026 at 10:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Machine learning uncovers how childhood trauma amplifies genetic risks for depression
Depression is a highly common mental health condition that affects millions of people worldwide. Medical professionals have established that the disorder arises from a combination of biological vulnerabilities and external stressors. A recent study published in the Proceedings of the National Academy of Sciences utilized a machine learning approach to map thousands of instances where early traumatic events amplify genetic risks for depression. The researchers discovered that childhood trauma exerts a profound influence on genetic susceptibility, highlighting biological connections that conventional statistical methods have routinely missed.
Scientists recognize that individual variations in human DNA do not entirely determine who will develop major depression. A person might carry specific genetic risk factors but never experience depressive symptoms unless they encounter severe environmental stress. This concept is often referred to as a gene by environment interaction. Researchers have struggled to identify these specific interactions because the genetic risks for depression are spread across hundreds of different locations within the human genome.
When scientists attempt to look for these interactions, they typically employ genome wide interaction studies. This standard method tests one genetic variant and one environmental factor at a time to see if they collectively influence a disease. Unfortunately, this one by one approach typically lacks the statistical power required to find subtle, nonlinear patterns scattered across so much genetic data. The sheer volume of tests creates statistical noise that obscures the true results.
Yue Hua, a biostatistician at the Yale University School of Public Health, led a research team to look past the limitations of the traditional approach. Hua and coauthors Jeffrey R. Gruen and Heping Zhang decided to analyze massive datasets using an advanced machine learning technique. They reasoned that algorithms could look at the data more holistically than standard linear equations.
The researchers turned to the UK Biobank, a large database containing genetic and health information from volunteers in the United Kingdom. After filtering the data for completeness and matching cases, they established a study group of 38,018 participants. Half of these individuals had a diagnosis of depression, and the other half served as a control group with no reported mental illness.
To measure environmental stress, the team used participant questionnaire responses regarding past traumatic experiences. They divided these experiences into three distinct categories. The categories included childhood trauma, adult trauma, and catastrophic trauma.
The genetic data consisted of over 285,000 single nucleotide polymorphisms. A single nucleotide polymorphism is a tiny, naturally occurring variation involving just one letter in a person’s DNA sequence. To search for connections between these genetic variations and the reported trauma, the team used an algorithm known as a random forest.
A random forest model operates by building hundreds of separate decision trees using random subsets of the data. Each tree attempts to predict whether an individual has depression by splitting the data according to genetic variants and trauma types. If a specific genetic variant and a specific type of trauma consistently end up next to each other on these decision pathways, the algorithm flags them as an interacting pair.
When the researchers ran a traditional genome wide interaction study on the data, the results were predictably flat. They found zero variations that met the threshold to be considered anything other than not statistically significant. This failure aligned with previous research efforts that struggled to find robust genetic interaction signals.
Applying the random forest method yielded a vastly different outcome. The algorithm identified 8,225 specific pairs where a genetic variation and a trauma exposure appeared to work together to increase depression risk. These variations mapped back to 1,732 unique genes across the human genome.
When classifying the results by trauma category, early life adversity stood out prominently. Childhood trauma was involved in the largest proportion of the identified genetic interactions. This suggests that trauma experienced during early developmental years plays a particularly potent role in unlocking genetic vulnerabilities.
To verify this pattern mathematically, the team calculated the heritability of depression for different subgroups in their study. Heritability is a statistical estimate of how much a trait is determined by genetic factors as opposed to environmental factors within a specific population.
For the individuals who reported experiencing childhood trauma, the estimated heritability of depression reached 13.3 percent. By comparison, the heritability estimate dropped to 6.0 percent for individuals who had not been exposed to childhood trauma. This difference mathematically demonstrates that genetic factors exert much more influence on depression when early life stress is present.
Adult and catastrophic trauma also showed mildly elevated heritability patterns compared to unexposed individuals. However, the differences for those remaining trauma categories were not statistically significant.
The researchers then focused on 22 top genes that showed the highest number of interactions with trauma in their machine learning model. A review of existing medical literature revealed that nearly all of these genes have been previously linked to psychiatric or neurological conditions. Some of the flagged genes are associated with bipolar disorder, memory function, and sleep disturbances.
To confirm that their algorithm was detecting real biological phenomena, the team tested their top findings in a completely different group of people. They accessed data from the Adolescent Brain Cognitive Development study, which tracks the health and development of children in the United States. Given that the participants were children aged nine and ten, the researchers focused exclusively on validating the childhood trauma interactions.
By running specialized genetic analyses on this separate cohort, the researchers replicated the interaction signals for 13 of the 22 top genes. Finding similar biological patterns in an independent group of American children provided secondary validation for the patterns originally detected in selectively older adult cohorts.
While the findings offer an expansive new view of depression, the researchers noted several limitations to their methodology. During the initial data sorting phase, hundreds of thousands of participants had to be removed from consideration because they skipped questions on the trauma surveys. This massive exclusion drastically reduced the sample size and could introduce unknown biases into the study population.
Another limitation rests within the mechanics of the random forest algorithm itself. The decision tree structure naturally favors variables that have very strong independent effects on an outcome. As a result, the algorithm might occasionally flag a gene and a trauma type as an interacting pair when they actually just have very strong, entirely separate impacts on depression.
Future scientific work will need to resolve these algorithmic gray areas. Researchers hope to use these machine learning findings as a screening step, which can then be followed by biological laboratory tests to verify exactly how a gene functions under stress. Identifying the exact cellular mechanisms could eventually open new pathways for treating stress related psychiatric conditions.
The study, “Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study,” was authored by Yue Hua, Jeffrey R. Gruen, and Heping Zhang.
-------------------------------------------------
DAILY EMAIL DIGEST: Email [email protected] -- no subject or message needed.
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
NYU Information for Practice puts out 400-500 good quality health-related research posts per week but its too much for many people, so that bot is limited to just subscribers. You can read it or subscribe at @PsychResearchBot
Since 1991 The National Psychologist has focused on keeping practicing psychologists current with news, information and items of interest. Check them out for more free articles, resources, and subscription information: https://www.nationalpsychologist.com
EMAIL DAILY DIGEST OF RSS FEEDS -- SUBSCRIBE: http://subscribe-article-digests.clinicians-exchange.org
READ ONLINE: http://read-the-rss-mega-archive.clinicians-exchange.org
It's primitive... but it works... mostly...
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #DepressionGenetics #ChildhoodTrauma #GeneEnvironmentInteraction #MachineLearningInMedicine #RandomForest #UKBiobank #AdolescentBrainCognitiveDevelopment #Heritability # PsychiatricGenomics #TraumaAndBiology
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https://www.europesays.com/africa/217507/ Clustering and machine learning techniques identify air pollution regimes in Greater Cairo #AirPollution #ClimateSciences #DecisionTrees #Egypt #EnvironmentalSciences #EnvironmentalSocialSciences #GreaterCairo #HumanitiesAndSocialSciences #KMeansClustering #MachineLearning #MathematicsAndComputing #multidisciplinary #RandomForest #science #UrbanEnvironments
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Comparative Analysis Of Seagrass Biophysical Properties Mapping Using Multi-Resolution Satellite Imagery And Machine Learning In The Shallow Waters Of Teluk Pandan, Lampung, Indonesia
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https://doi.org/10.1016/j.rsase.2026.102002 <-- shared paper
--
#GIS #spatial #mapping #Seagrass #monitoring #conservation #accuracy #vegetation #biophysical #benthic #habitat #composition #carbonmapping #Randomforest #ExtremeGradientBoosting #XGBoost algorithms #Sentinel2 #remotesensing #sensor #shallowwater #sentinel #PlanetScope #satellite #TelukPandan #Lampung #Indonesia #AI #deeplearning #machinelearning #model #modeling #water #marine #ocean #habitat #ecosystem #spatialanalysis -
Comparative Analysis Of Seagrass Biophysical Properties Mapping Using Multi-Resolution Satellite Imagery And Machine Learning In The Shallow Waters Of Teluk Pandan, Lampung, Indonesia
--
https://doi.org/10.1016/j.rsase.2026.102002 <-- shared paper
--
#GIS #spatial #mapping #Seagrass #monitoring #conservation #accuracy #vegetation #biophysical #benthic #habitat #composition #carbonmapping #Randomforest #ExtremeGradientBoosting #XGBoost algorithms #Sentinel2 #remotesensing #sensor #shallowwater #sentinel #PlanetScope #satellite #TelukPandan #Lampung #Indonesia #AI #deeplearning #machinelearning #model #modeling #water #marine #ocean #habitat #ecosystem #spatialanalysis -
Comparative Analysis Of Seagrass Biophysical Properties Mapping Using Multi-Resolution Satellite Imagery And Machine Learning In The Shallow Waters Of Teluk Pandan, Lampung, Indonesia
--
https://doi.org/10.1016/j.rsase.2026.102002 <-- shared paper
--
#GIS #spatial #mapping #Seagrass #monitoring #conservation #accuracy #vegetation #biophysical #benthic #habitat #composition #carbonmapping #Randomforest #ExtremeGradientBoosting #XGBoost algorithms #Sentinel2 #remotesensing #sensor #shallowwater #sentinel #PlanetScope #satellite #TelukPandan #Lampung #Indonesia #AI #deeplearning #machinelearning #model #modeling #water #marine #ocean #habitat #ecosystem #spatialanalysis -
Comparative Analysis Of Seagrass Biophysical Properties Mapping Using Multi-Resolution Satellite Imagery And Machine Learning In The Shallow Waters Of Teluk Pandan, Lampung, Indonesia
--
https://doi.org/10.1016/j.rsase.2026.102002 <-- shared paper
--
#GIS #spatial #mapping #Seagrass #monitoring #conservation #accuracy #vegetation #biophysical #benthic #habitat #composition #carbonmapping #Randomforest #ExtremeGradientBoosting #XGBoost algorithms #Sentinel2 #remotesensing #sensor #shallowwater #sentinel #PlanetScope #satellite #TelukPandan #Lampung #Indonesia #AI #deeplearning #machinelearning #model #modeling #water #marine #ocean #habitat #ecosystem #spatialanalysis -
Comparative Analysis Of Seagrass Biophysical Properties Mapping Using Multi-Resolution Satellite Imagery And Machine Learning In The Shallow Waters Of Teluk Pandan, Lampung, Indonesia
--
https://doi.org/10.1016/j.rsase.2026.102002 <-- shared paper
--
#GIS #spatial #mapping #Seagrass #monitoring #conservation #accuracy #vegetation #biophysical #benthic #habitat #composition #carbonmapping #Randomforest #ExtremeGradientBoosting #XGBoost algorithms #Sentinel2 #remotesensing #sensor #shallowwater #sentinel #PlanetScope #satellite #TelukPandan #Lampung #Indonesia #AI #deeplearning #machinelearning #model #modeling #water #marine #ocean #habitat #ecosystem #spatialanalysis -
【🎉Latest accepted article】
Enhancing Forest Biomass Estimation with Synthetic #AirborneLaserScanning via Voxel-based Forest Reconstruction#AbovegroundBiomass | #LiDARSimulation | #VirtualForest | #RandomForest
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【🎉Latest accepted article】
Enhancing Forest Biomass Estimation with Synthetic #AirborneLaserScanning via Voxel-based Forest Reconstruction#AbovegroundBiomass | #LiDARSimulation | #VirtualForest | #RandomForest
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【🎉Latest accepted article】
Enhancing Forest Biomass Estimation with Synthetic #AirborneLaserScanning via Voxel-based Forest Reconstruction#AbovegroundBiomass | #LiDARSimulation | #VirtualForest | #RandomForest
-
【🎉Latest accepted article】
Enhancing Forest Biomass Estimation with Synthetic #AirborneLaserScanning via Voxel-based Forest Reconstruction#AbovegroundBiomass | #LiDARSimulation | #VirtualForest | #RandomForest
-
【🎉Latest accepted article】
Enhancing Forest Biomass Estimation with Synthetic #AirborneLaserScanning via Voxel-based Forest Reconstruction#AbovegroundBiomass | #LiDARSimulation | #VirtualForest | #RandomForest
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The Statistics Globe Hub starts in 3 days, and I would like to give you a short preview of the first module "Feature Selection Using Random Forest."
Interested in joining the Hub? You can find more information here: https://statisticsglobe.com/hub
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Geomorphic Factors Impact Groundwater Levels More Than Harvesting in a Coast Redwood Forest
--
https://doi.org/10.1002/hyp.70302 <-- shared paper
--
#water #hydrology #groundwater #stream #baseflow #plant #gegetation #transpiration #growingseason #forest #management #watershed #mediterraneanclimate #California #redwood #harvesting #soils #geomorphology #aquiferdepth #slope #randomforest #model #modeling #coast #coastal #rainfill #precipitation #infiltration
#USFS -
Geomorphic Factors Impact Groundwater Levels More Than Harvesting in a Coast Redwood Forest
--
https://doi.org/10.1002/hyp.70302 <-- shared paper
--
#water #hydrology #groundwater #stream #baseflow #plant #gegetation #transpiration #growingseason #forest #management #watershed #mediterraneanclimate #California #redwood #harvesting #soils #geomorphology #aquiferdepth #slope #randomforest #model #modeling #coast #coastal #rainfill #precipitation #infiltration
#USFS -
Geomorphic Factors Impact Groundwater Levels More Than Harvesting in a Coast Redwood Forest
--
https://doi.org/10.1002/hyp.70302 <-- shared paper
--
#water #hydrology #groundwater #stream #baseflow #plant #gegetation #transpiration #growingseason #forest #management #watershed #mediterraneanclimate #California #redwood #harvesting #soils #geomorphology #aquiferdepth #slope #randomforest #model #modeling #coast #coastal #rainfill #precipitation #infiltration
#USFS -
Geomorphic Factors Impact Groundwater Levels More Than Harvesting in a Coast Redwood Forest
--
https://doi.org/10.1002/hyp.70302 <-- shared paper
--
#water #hydrology #groundwater #stream #baseflow #plant #gegetation #transpiration #growingseason #forest #management #watershed #mediterraneanclimate #California #redwood #harvesting #soils #geomorphology #aquiferdepth #slope #randomforest #model #modeling #coast #coastal #rainfill #precipitation #infiltration
#USFS -
Geomorphic Factors Impact Groundwater Levels More Than Harvesting in a Coast Redwood Forest
--
https://doi.org/10.1002/hyp.70302 <-- shared paper
--
#water #hydrology #groundwater #stream #baseflow #plant #gegetation #transpiration #growingseason #forest #management #watershed #mediterraneanclimate #California #redwood #harvesting #soils #geomorphology #aquiferdepth #slope #randomforest #model #modeling #coast #coastal #rainfill #precipitation #infiltration
#USFS -
🌳 Random Forests and Living Trees
English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.
What started as a local research project in Kryvyi Rih turned into something much larger — the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.
The methodology developed during that work is still being used today — adapted for new environmental and urban projects.
🔗 https://www.datastory.org.ua/random-forests-and-living-trees/
#RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih #LULC
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🌳 Random Forests and Living Trees
English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.
What started as a local research project in Kryvyi Rih turned into something much larger — the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.
The methodology developed during that work is still being used today — adapted for new environmental and urban projects.
🔗 https://www.datastory.org.ua/random-forests-and-living-trees/
#RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih #LULC
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🌳 Random Forests and Living Trees
English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.
What started as a local research project in Kryvyi Rih turned into something much larger — the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.
The methodology developed during that work is still being used today — adapted for new environmental and urban projects.
🔗 https://www.datastory.org.ua/random-forests-and-living-trees/
#RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih #LULC
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🌳 Random Forests and Living Trees
English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.
What started as a local research project in Kryvyi Rih turned into something much larger — the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.
The methodology developed during that work is still being used today — adapted for new environmental and urban projects.
🔗 https://www.datastory.org.ua/random-forests-and-living-trees/
#RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih
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🌳 Random Forests and Living Trees
English translation of my earlier article on applying satellite imagery and machine learning to map urban land cover.
What started as a local research project in Kryvyi Rih turned into something much larger — the results sparked a heated discussion among residents, officials, and industry representatives about the real condition of green buffers around large industrial sites.
The methodology developed during that work is still being used today — adapted for new environmental and urban projects.
🔗 https://www.datastory.org.ua/random-forests-and-living-trees/
#RemoteSensing #MachineLearning #LandCoverMapping #UrbanEcology #EnvironmentalMonitoring #RandomForest #GeospatialAnalysis #GIS #RStats #SAGAGIS #QGIS #IndependentResearch #OpenSource #EnvironmentalDataScience #KryvyiRih
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Machine learning-based prediction of compressive energy absorption in shoe soles with different features https://www.byteseu.com/1478481/ #CompressionTest #CurveFitting #Energy #FlexibleSoles #HumanitiesAndSocialSciences #MechanicalEngineering #MLP #multidisciplinary #Orthopaedics #RandomForest #Science #SVR
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Assessment Of Snow Cover Dynamics And The Effects Of Environmental Drivers In High Mountain Ecosystems
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https://doi.org/10.1016/j.eiar.2025.107969 <-- shared paper
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#GIS #spatial #mapping #remotesensing #earthobservation #snow #ice #snowcover #dynamics #climatechange #mountains #ecosystems #spatialanalysis #spatiotemporal #MODIS #model #modeling #extremeweather #water #hydrology #climate #zones #trendanalysis #linearregression #RandomForest #cryosphere -
Avalanche Debris Detection From Sentinel-2 Data Using Fuzzy Machine Learning And Colour Spaces For The Indian Himalaya
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared paper
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#GIS #spatial #mapping #snowavalanches #snow #avalanches #machinelearning #fuzzyclassification #SVM #AI #randomforest #model #modeling #forecasting #risk #hazard #massmovement #engineeringgeology #remotesensing #earthobservation #imagery #spatialanalysis #spatiotemporal #change #debris #detection #satellite #sentinel #Himalaya #Himalayas #performance -
Avalanche Debris Detection From Sentinel-2 Data Using Fuzzy Machine Learning And Colour Spaces For The Indian Himalaya
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared paper
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#GIS #spatial #mapping #snowavalanches #snow #avalanches #machinelearning #fuzzyclassification #SVM #AI #randomforest #model #modeling #forecasting #risk #hazard #massmovement #engineeringgeology #remotesensing #earthobservation #imagery #spatialanalysis #spatiotemporal #change #debris #detection #satellite #sentinel #Himalaya #Himalayas #performance -
Avalanche Debris Detection From Sentinel-2 Data Using Fuzzy Machine Learning And Colour Spaces For The Indian Himalaya
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared paper
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#GIS #spatial #mapping #snowavalanches #snow #avalanches #machinelearning #fuzzyclassification #SVM #AI #randomforest #model #modeling #forecasting #risk #hazard #massmovement #engineeringgeology #remotesensing #earthobservation #imagery #spatialanalysis #spatiotemporal #change #debris #detection #satellite #sentinel #Himalaya #Himalayas #performance -
Avalanche Debris Detection From Sentinel-2 Data Using Fuzzy Machine Learning And Colour Spaces For The Indian Himalaya
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https://doi.org/10.1080/2150704X.2025.2488532 <-- shared paper
--
#GIS #spatial #mapping #snowavalanches #snow #avalanches #machinelearning #fuzzyclassification #SVM #AI #randomforest #model #modeling #forecasting #risk #hazard #massmovement #engineeringgeology #remotesensing #earthobservation #imagery #spatialanalysis #spatiotemporal #change #debris #detection #satellite #sentinel #Himalaya #Himalayas #performance -
This series of videos on machine learning algorithms (Lab a through Lab d, so far) by Courage Kamusoko are the best explanations I've seen yet. How the models actually work, their strengths and weaknesses, what you are actually solving for when you tune the hyperparameters, and examples in Python. https://www.youtube.com/@couragekamusoko5689/videos
#SVM #KNN #DecisionTree #RandomForest -
This series of videos on machine learning algorithms (Lab a through Lab d, so far) by Courage Kamusoko are the best explanations I've seen yet. How the models actually work, their strengths and weaknesses, what you are actually solving for when you tune the hyperparameters, and examples in Python. https://www.youtube.com/@couragekamusoko5689/videos
#SVM #KNN #DecisionTree #RandomForest -
This series of videos on machine learning algorithms (Lab a through Lab d, so far) by Courage Kamusoko are the best explanations I've seen yet. How the models actually work, their strengths and weaknesses, what you are actually solving for when you tune the hyperparameters, and examples in Python. https://www.youtube.com/@couragekamusoko5689/videos
#SVM #KNN #DecisionTree #RandomForest -
This series of videos on machine learning algorithms (Lab a through Lab d, so far) by Courage Kamusoko are the best explanations I've seen yet. How the models actually work, their strengths and weaknesses, what you are actually solving for when you tune the hyperparameters, and examples in Python. https://www.youtube.com/@couragekamusoko5689/videos
#SVM #KNN #DecisionTree #RandomForest -
Machine Learning – Regression Cheat Sheet | How To Perform Regression
Learn about machine learning regression algorithms, tools, & tips #xgboost #randomforest #decisiontree #svm #glm #gbm. source
https://quadexcel.com/wp/machine-learning-regression-cheat-sheet-how-to-perform-regression/
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If I were running a blog on applying #randomforest models to various problems, I would call it The Statistical Lumberjack 🤔
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I scaled up the popular Palmer Penguins machine learning dataset from 344 rows to 100k rows using adversarial random forest, with an accuracy of 88%.
Now, you have more rows of data with which to train your classification models.
You can download it here, along with R & Python scripts, to load and view the dataset: https://ieee-dataport.org/documents/palmer-penguins-100k-0
Have a dataset you want to scale up? Say hello!
#machinelearning #randomforest #rstats #python #datascience #datasets #syntheticdatageneration #ai
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Essentially, each path in a #RandomForest to a leaf indicates that a number of training examples satisfy a sequence of constraints (from the splits). Inferring training data boils down to finding a set of examples satisfying all these constraints, a bit like placing numbers on a Sudoku...
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"Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers"
https://arxiv.org/abs/2402.01502
'... Despite their remarkable effectiveness and broad application, the drivers of success underlying ensembles of trees are still not fully understood. In this paper, we highlight how interpreting tree ensembles as adaptive and self-regularizing smoothers can provide new intuition and deeper insight to this topic...'
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One question for the #MachineLearning people: what approach do you use to determine if a decision trees or a random forest approach should work better? Do you simply try both approaches and use whatever seems to work better?
According to what I read, decision trees are more prone to overfitting, while random forest is a more complex approach. Which means little to me 😅
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Here is an example of using #RandomForests 🌳🌳 for #PixelClassification 🖼️ in #Python 🐍, using @napari for labeling ✍️
🌎 https://www.fabriziomusacchio.com/blog/2023-06-23-_random_forests_pixel_classifier/
#RandomForest #Napari #MachineLearning #ImageProcessing #Bioimage #BioimageAnalysis
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Ever wondered how #DecisionTrees and #RandomForests 🌳🌳 are related? Here is a quick #tutorial that compares both methods in terms of #classification and #regression ✌️
🌎 https://www.fabriziomusacchio.com/blog/2023-06-22-_decision_trees_vs_random_forests/
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How to use #randomForest for predicting community interactions
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New paper from @GlobEcoFlinders
'Predicting predator–prey interactions in terrestrial endotherms using #randomForest'
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Area Estimation of Mango and Coconut Crops using Machine Learning in Hesaraghatta Hobli of Bengaluru Urban District, Karnataka [India]
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https://doi.org/10.58825/jog.2023.17.1.75 <-- shared paper
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#GIS #spatial #mapping #spatialanalysis #Mango #Coconut #Areaestimation #Machinelearning #ConvolutionalNeuralNetwork #CNN #RandomForest #RF #agricultural #agriculture #farming #estimates #Karnataka #India #acreage #production #horticulture #commodities #export #remotesensing #satellite #GoogleEarthEngine #GEE #Copernicus #Sentinel2 #algorithms #learning #google #ml -
Our paper about the comparison of #machineLearning and regression analysis in predicting #sexual reoffenses will be published in "#Assessment"
Guess what? "#RandomForest Does Not Outperform Logistic Regression in the Prediction of Sexual Recidivism"With Sonja Etzler, @[email protected], @florianpargent
Preprint: https://psyarxiv.com/z6ky2 -
Interesting new study estimating the #replicability of published research in #psychology over the past 20 years.
The paper includes *nearly all papers* published in six top psychology #journals over last 2 decades.
https://www.pnas.org/doi/10.1073/pnas.2208863120
The researchers used a #MachineLearning model (#RandomForest & logistic regression ensemble) to estimate the replication likelihood of over 14,000 #articles from 2000-2019 in six subfields of psychology.
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Beautiful
```
struct RandomForest {
nodes: []Node
num_trees: i32
}struct Node {
feature: i32
threshold: f64
left: *Node
right: *Node
}
``` -
Why stop at one decision tree when you can have a whole random forest? 🌲 🎄 🌳 😄
On Monday Dec 5, we will learn all about random forests during our R-Ladies bookclub session ▶️ Ch 11 from the book Hands on Machine Learning with R by Bradley Boehmke & Brandon Greenwell
Anyone who's interested can join! Sign up via Meetup 👇
https://www.meetup.com/rladies-den-bosch/events/290101567/#rstats #rladies #bookclub #MachineLearning #RandomForest #RLadiesDenBosch #RLadiesUtrecht