Decision Trees - VisuallyExplained скачать в хорошем качестве

Decision Trees - VisuallyExplained

Introduction to Decision Trees For Classification Problems with a Python Example. #decisiontree #python #classification #datascience #statistics Code snippets used in the video: ``` Install required packages pip install pandas scikit-learn Download Pokemon dataset wget -q https://gist.githubusercontent.com/ar...\ 194bcff35001e7eb53a2a8b441e8b2c6/raw/\ 92200bc0a673d5ce2110aaad4544ed6c4010f687/pokemon.csv Load dataset import pandas as pd df = pd.read_csv("pokemon.csv").rename(columns={"Type 1": "Type"}) Filter two types only data = data.query("Type.isin(('Electric', 'Grass'))") Training Dataset X = data[['HP', 'Attack', 'Defense', 'Speed', ]] # Features y = (data['Type'] == 'Electric') # = 0 if Grass, = 1 if Electric Train decision tree from sklearn.tree import DecisionTreeClassifier tree = DecisionTreeClassifier(max_depth=1).fit(X, y) Plot decision tree from sklearn.tree import plot_tree plot_tree(tree); Predict using the decision tree predictions = tree.predict(X) predictions[3] # is Pokemon at index 3 of type "Electric"? Accuracy score from sklearn.metrics import accuracy_score accuracy_score(y, tree.predict(X)) change depth to 2 tree = DecisionTreeClassifier(max_depth=2).fit(X, y) ``` -------------------------- This video would not have been possible without the help of Gökçe Dayanıklı.