Master Decision Trees and Random Forests with Scikit-learn

Master Decision Trees and Random Forests with Scikit-learn, available at $39.99, has an average rating of 4.3, with 101 lectures, 22 quizzes, based on 5 reviews, and has 1223 subscribers.

You will learn about 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. This course is ideal for individuals who are Professionals, students, anybody who wants to use decision trees and random forests for making predictions with data. or 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. or Professionals, students, anybody interested in doing prediction projects with the Python Scikit-learn library using decision trees or random forests. It is particularly useful for Professionals, students, anybody who wants to use decision trees and random forests for making predictions with data. or 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. or Professionals, students, anybody interested in doing prediction projects with the Python Scikit-learn library using decision trees or random forests.

Enroll now: Master Decision Trees and Random Forests with Scikit-learn

Summary

Title: Master Decision Trees and Random Forests with Scikit-learn

Price: $39.99

Average Rating: 4.3

Number of Lectures: 101

Number of Quizzes: 22

Number of Published Lectures: 101

Number of Published Quizzes: 22

Number of Curriculum Items: 123

Number of Published Curriculum Objects: 123

Original Price: $29.99

Quality Status: approved

Status: Live

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.

Who Should Attend

• 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.

Target Audiences

• 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.