機器學習100天系列學習筆記 機器學習100天(中文翻譯版)機器學習100天(英文原版）

第一步：導包

#Step 1: Data Preprocessing import pandas as pd import numpy as np import matplotlib.pyplot as plt

第二步：導入數據

# 28個樣本 dataset = pd.read_csv('D:/daily/機器學習100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/studentscores.csv') X = dataset.iloc[ : , :-1].values Y = dataset.iloc[ : , 1 ].values

第三步：劃分訓練集、測試集

#Step 3: Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)

第四步：簡單線性回歸擬合

#Step 4: Fitting Simple Linear Regression Model to the training set from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor = regressor.fit(X_train, Y_train)

第五步：預測

#Step 5: Predecting the Result Y_pred = regressor.predict(X_test)

第六步：訓練集可視化

#Step 6: Visualising the Training results plt.scatter(X_train , Y_train, color = 'red') plt.plot(X_train , regressor.predict(X_train), color ='blue') plt.show()

第七步：測試集可視化

#Step 7: Visualizing the test results plt.scatter(X_test , Y_test, color = 'red') plt.plot(X_test , regressor.predict(X_test), color ='blue') plt.show()

第八步：回歸性能指標

#Step 8: regression evaluation from sklearn.metrics import r2_score y_pred = regressor.predict(X_test) print(r2_score(Y_test, y_pred))

打印：0.30574547147699993

R2 決定系數（擬合優度），模型越好：r2→1；模型越差：r2→0

完整代碼：

#Day 2: Simple Linear Regression 2022/4/5 #Step 1: Data Preprocessing import pandas as pd import numpy as np import matplotlib.pyplot as plt#Step 2: Importing dataset #28個樣本 dataset = pd.read_csv('D:/daily/機器學習100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/studentscores.csv') X = dataset.iloc[ : , :-1].values Y = dataset.iloc[ : , 1 ].values#Step 3: Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)#Step 4: Fitting Simple Linear Regression Model to the training set from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor = regressor.fit(X_train, Y_train)#Step 5: Predecting the Result Y_pred = regressor.predict(X_test)#Step 6: Visualising the Training results plt.scatter(X_train , Y_train, color = 'red') plt.plot(X_train , regressor.predict(X_train), color ='blue') plt.show()#Step 7: Visualizing the test results plt.scatter(X_test , Y_test, color = 'red') plt.plot(X_test , regressor.predict(X_test), color ='blue') plt.show()#Step 8: regression evaluation from sklearn.metrics import r2_score y_pred = regressor.predict(X_test) print(r2_score(Y_test, y_pred))

總結

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