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Multi-Target Prediction with Trees and Tree Ensembles
Published on Jun 28, 2019163 Views
Increasingly often, we need to learn predictive models from big or complex data, which may comprise many examples and many input/output dimensions. When more than one target variable has to be predict
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Chapter list
Multi-target Prediction with Trees & Tree Ensembles00:00
The basic Machine Learning task: Predictive modeling01:09
An example task of Predictive Modelling: Medical diagnosis01:36
Example task: Descriptive vars.; Biomarkers for Alzheimer’s02:06
Example: Decision tree for diagnosis02:37
Another example of single-target predictive modeling (classification)03:02
Another example of single-target predictive modeling (classification) - 204:13
What is a decision tree?05:31
Making a Prediction with a Decision Tree05:54
Top-Down Induction of Decision Trees06:43
TDIDT Illustrated08:57
Mining Big and Complex Data: Dimensions of Complexity11:29
Big Data: Volume & Velocity13:56
Data streams: Regression15:12
Big Data: Variety - Structured Input15:28
Big Data: Variety - Structured Input - 216:34
Semi-supervised learning: Classification and regression18:10
Data in context: Spatio-temporal, network19:27
The Different Tasks of Multi-Target Prediction20:39
Weather prediction20:52
Multi-target prediction21:58
Example MTR task: Target vars.; Clinical scores for Alzheimer’s22:21
Example MTR model23:30
Multi-Target Classification & Multi-Label Classification24:13
Multi-Label Classification Example24:52
Hierarchical multi-label classification25:18
Hierarchical multi-label classification - 225:56
Hierarchical multi-target regression26:44
Mining Big and Complex Data: Combining Complexities28:02
SSL+SOP: Incomplete Annotations28:05
Data streams: (MT) Regression29:06
Network +SOP: HMC29:21
Predictive Clustering for Multi-Target Prediction29:55
Clustering30:16
Example predictive clustering tree31:12
Top-down induction of PCTs32:48
Learning PCTs34:22
Learning PCTs - 235:05
Selecting the best test in a PCT36:52
Multi-target regression37:06
Ensembles of PCTs37:31
Relating the Environment and the Biota: From Habitat models to Community composition38:20
Environment <-> Biota38:42
Habitat modeling38:53
Predicting species composition39:18
Predicting community structure39:30
Community structure: Overall results40:09
SSL in MTP: Accuracy & interpretability42:19
Multi-Target Prediction for Virtual Compound Screening44:43
Virtual compound screening44:48
Host-targeted Drugs for MTB (Tuberculosis) and STM (Salmonella)45:33
MTB&STM: Host-targeted Drugs46:14
MTB&STM: Host-targeted Drugs The Data Analysis Workflow47:14
MTB&STM: Host-targeted Drugs Results47:58
Analyzing data from high-contents screens48:47
Reducing fibrosis in myocardial infarction49:07
Testing the predictions49:19
Conclusions49:38