Master Decision Trees and Random Forests with Scikit-learn
Get to the bottom of how to make predictions with them and enjoy your competitive edge. Jupyter Notebooks included.
4.39 (9 reviews)
1,250
students
4 hours
content
May 2023
last update
$39.99
regular price
What you will 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.
Predict purchases and prices with decision trees and random forests.
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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4844944
udemy ID
8/22/2022
course created date
8/26/2022
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