Random Forest is an example of ensemble learning where each model is a decision tree and their predictions are aggregated to identify the most popular result. Random forest only select a random subset of features from the original data to make predictions.
In random forest the decision trees are trained independent to each other.
Classes, functions, and methods:
from sklearn.ensemble import RandomForestClassifier: random forest classifier from sklearn ensemble class.plt.plot(x, y): draw line plot for the values of y against x values.
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- bootstrapping: training on a subset of the observations
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