端到端機器學習
Joshua Stapleton
Machine Learning Engineer
Logistic Regression
sklearn.linear_model.LogisticRegression
Support Vector Classifier
sklearn.svm.SVC
Decision Tree
sklearn.tree.DecisionTreeClassifier
Random Forest
sklearn.ensemble.RandomForestClassifier
深度學習模型
K-Nearest Neighbors (KNN)
XGBoost
模型：
原則：
sklearn.model_selection.train_test_split
# 匯入必要的函式庫 from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression # 將資料分成訓練集與測試集（80:20） X_train, X_test, y_train, y_test = train_test_split(features, heart_disease_y, test_size=0.2, random_state=42) # 定義模型 logistic_model = LogisticRegression(max_iter=200) # 訓練模型 logistic_model.fit(X_train, y_train)
# 匯入必要的函式庫 from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression
# 將資料分成訓練集與測試集（80:20） X_train, X_test, y_train, y_test = train_test_split(features, heart_disease_y, test_size=0.2, random_state=42)
# 定義模型 logistic_model = LogisticRegression(max_iter=200)
# 訓練模型 logistic_model.fit(X_train, y_train)
# Jane Doe 的健康資料，例如：[年齡、膽固醇、血壓等] jane_doe_data = [45, 230, 120, ...] # 轉為 2D，因為 scikit-learn 需要 2D 類陣列輸入 jane_doe_data = jane_doe_data.reshape(1, -1) # 使用模型預測 Jane 的心臟病診斷機率 jane_doe_probabilities = logistic_model.predict_proba(jane_doe_data) jane_doe_prediction = logistic_model.predict(jane_doe_data)
# Jane Doe 的健康資料，例如：[年齡、膽固醇、血壓等] jane_doe_data = [45, 230, 120, ...]
# 轉為 2D，因為 scikit-learn 需要 2D 類陣列輸入 jane_doe_data = jane_doe_data.reshape(1, -1)
# 使用模型預測 Jane 的心臟病診斷機率 jane_doe_probabilities = logistic_model.predict_proba(jane_doe_data) jane_doe_prediction = logistic_model.predict(jane_doe_data)
# 印出機率 print(f"Jane Doe's predicted probabilities: {jane_doe_probabilities[0]}") print(f"Jane Doe's predicted health condition: {jane_doe_prediction[0]}")
Jane Doe's predicted health condition probabilities: [0.2 0.8] Jane Doe's predicted health condition: 1
Jane Doe's predicted health condition probabilities: [0.2 0.8]
Jane Doe's predicted health condition: 1
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