Decision Trees, Random Forests: get ready with Python
Master the use of decision trees and random forests for data analysis projects with Scikit-learn. Learn prediction techniques and improve regression models.
What you’ll learn
- Learn how decision trees and random forests make their predictions.
- Learn how to use Scikit-learn for prediction with decision trees and random forests and for understanding the predictive structure of data sets.
- Learn how to do your own prediction project with decision trees and random forests using Scikit-learn.
- Learn about each parameter of Scikit-learn’s methods DecisonTreeClassifier and RandomForestClassifier to define your decision tree or random forest.
- Learn using the output of Scikit-learn’s DecisonTreeClassifier and RandomForestClassifier methods to investigate and understand your predictions.
- Learn about how to work with imbalanced class values in the data and how noisy data can affect random forests’ prediction performance.
- Growing decision trees: node splitting, node impurity, Gini diversity, entropy, mean squared and absolute error, Poisson deviance, feature thresholds.
- Improving decision trees: cross-validation, grid/randomized search, tuning and minimal cost-complexity pruning, evaluating feature importance.
- Creating random forests: bootstrapping, bagging, random feature selection, decorrelation of tree predictions.
- Improving random forests: cross-validation, grid/randomized search, tuning, out-of-bag scoring, calibration of probability estimates.
- Learn to use Scikit-learn’s methods DecisonTreeRegressor and RandomForestRegressor to fit and improve your regression decision tree or random forest.
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The lessons of this course help you mastering the use of decision trees and random forests for your data analysis projects. You will learn how to address classification and regression problems with decision trees and random forests. The course focuses on decision tree classifiers and random forest classifiers because most of the successful machine learning applications appear to be classification problems. The lessons explain:
Decision trees for classification and regression problems.
Elements of growing decision trees.
The sklearn parameters to define decision tree classifiers and regressors.
Prediction with decision trees using Scikit-learn (fitting, pruning/tuning, investigating).
The sklearn parameters to define random forest classifiers and regressors.
Prediction with random forests using Scikit-learn (fitting, tuning, investigating).
The ideas behind random forests for prediction.
Characteristics of fitted decision trees and random forests.
Importance of data and understanding prediction performance.
How you can carry out a prediction project using decision trees and random forests.
Focusing on classification problems, the course uses the DecisionTreeClassifier and RandomForestClassifier methods of Python’s Scikit-learn library to explain all the details you need for understanding decision trees and random forests. It also explains and demonstrates Scikit-learn’s DecisionTreeRegressor and RandomForestRegressor methods to adress regression problems. It prepares you for using decision trees and random forests to make predictions and understanding the predictive structure of data sets.
This is what is inside the lessons:
This course is for people who want to use decision trees or random forests for prediction with Scikit-learn. This requires practical experience and the course facilitates you with Jupyter notebooks to review and practice the lessons’ topics.
Each lesson is a short video to watch. Most of the lessons explain something about decision trees or random forests with an example in a Jupyter notebook. The course materials include more than 50 Jupyter notebooks and the corresponding Python code. You can download the notebooks of the lessons for review. You can also use the notebooks to try other definitions of decision trees and random forests or other data for further practice.
Who this course is for:
- Professionals, students, anybody who wants to use decision trees and random forests for making predictions with data.
- Professionals, students, anybody who works with data on projects and wants to know more about decision trees or random forest after an initial experience using them.
- Professionals, students, anybody interested in doing prediction projects with the Python Scikit-learn library using decision trees or random forests.
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