Random Forest with Python: Beginner To Advanced Course

Goals

What will you learn in this course:

• 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.

Prerequisites

What are the prerequisites for this course?

• 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).