Decision Trees, Random Forests: get ready with Python

Learn to make and understand predictions with decision trees and random forests. Includes detailed Python demos.

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, impurity reduction, feature thresholds.
Improving decision trees: cross-validation, grid/randomized search, tuning and minimum 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.

Requirements
You should be comfortable with reading and following Python code in Jupyter notebooks representing data descriptions, estimation or model fitting and data analysis output (using Python libraries: pandas, numpy, scikit-learn, matplotlib).
To fully benefit from the course you should be able to run the Jupyter notebooks or Python programs of the lessons.
You’ll need to know some elementary statistics to follow all the lessons (random variable, probability distribution, histogram, boxplot). The lessons are easier to follow if you already have some general idea of supervised learning or classification problems.
Description
The lessons of this course help you mastering the use of decision trees and random forests for your data analysis projects. 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 problems.

Elements of growing decision trees.

The sklearn parameters to define decision tree classifiers.

Prediction with decision trees using Scikit-learn (fitting, pruning/tuning, investigating).

The sklearn parameters to define random forest classifiers.

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. 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.