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Elementary learning notes for Data Science, A.I. and Machine Learning
Get in mind that I'm using... from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split( data, target, test_size=0.2, random_state=42 ) note that cross_validate() is for option. alternatively, grid...
Decision Tree doesn't need scaled data first phrase : condition gini : impurity score samples : total sample size in this node value : [negative sample size, positive sample size] Left child : positive, Right child : negative Impurity Gini impu...
Get in mind that I'm using... from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(data, target, random_state=42) from sklearn.preprocessing import StandardScaler ss = StandardScaler() ss.fit(x_tra...
Get in mind that I'm using... # checkout variable names. from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(<data>, <target>) Transformer sklearn.preprocessing.PolynomialFeatures adds additiona...