What you would learn in Decision Trees, Random Forests: get ready with Python course?
The lessons in this course can help you master the application of random and decision tree forests to aid in projects in data analysis. The class focuses on classifiers for decision trees and random forest classifiers as the bulk of the successful machine learning applications are classified issues. The course explains:
Decision trees for classification issues.
The elements of growing decision trees.
The parameters used by sklearn to create classifiers for decision trees.
Prediction of decision trees using Scikit-learn (fitting and tuning, pruning/tuning, analyzing).
The sklearn parameters are used to create random forest classification algorithms.
Prediction using random forests with Scikit-learn (fitting and tuning, analyzing).
Random forests are the basis for predicting.
Specific characteristics of the fitted decision tree and random forests.
Data is essential and can help in understanding predictive performance.
How can you carry out an idea of prediction using random forests and decision trees?
Focusing on classification problems, the course uses the DecisionTreeClassifier and RandomForestClassifier methods of Python's Scikit-learn library. It prepares you to use random forests and decision trees to make predictions and to understand the structure of predictive data sets.
This is what's in the lesson:
This course is designed for those interested in using random forests or decision trees for prediction using Scikit-learn. It requires experience, and the course will provide students with Jupyter notebooks to help them review and practice the lesson's subjects.
Every lesson is a short video to view. Most lessons cover something about decision trees, or random forests, with an example from the form of a Jupyter notebook. The course materials consist of more than 50 notebooks in Jupyter and Python code. Notebooks can be downloaded of the lessons to review. You can also utilize the notebooks for different definitions of decision trees, random forests, or any other data to further improve your training.
Learn how decision trees, as well as random forests, come up with their predictions.
Learn to use Scikit-learn to predict using random and decision tree forests and understand how data sets predictive structures to work.
Learn how to make your prediction projects using random forests and decision trees with 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 how to deal with using class values that are not balanced in the data, as well as how noisy data could affect the prediction of random forests.
Decision trees are growing: splitting nodes node impurity Gini variety, diversity features thresholds, impurity reduction.
Enhancing decision trees by cross-validation, grid/randomized search tuning, and minimal cost-complexity trimming by assessing the importance of features.
Random forests are created by bags, bootstrapping random feature selection trees and their predictions.
Improved random forests through cross-validation, grid/randomized search tuning, and out-of-bag scoring the calibration of probabilities estimates.
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