# 选择用逻辑回归 # 1)导入逻辑回归算法 from sklearn.linear_model import LogisticRegression # 2)创建模型 model = LogisticRegression() # 3)训练模型 model.fit(train_X, train_y) # 4) 模型评估 model.score(test_X , test_y ) 0.765363128491
Model is build on Simple linear regression and later optimized by Random forest Algorithm. regression prediction randomforest linear kaggledatasets Updated Dec 25, 2019 Jupyter Notebook Sanya-Dechamma / DataAnalysisProjects Star 0 Code Issues Pull requests A collection of data analysis ...
Linear regression,Ridge,Lasso models Week4 Tree based models,Xgboost and LightGBM (1)Python Tutorial #7: Decision Trees (2)Gini impurity in decision tree CART algorithm (3)Python Tutorial #8: Xgboost & LightGBM (4)Gradient boosting explained 2 Advanced Models/algorithms Week 5 DNN Week 6 ...
Projects: Analyze a dataset (Kaggle or open-source data) to practice EDA (Exploratory Data Analysis). Phase 3: Machine Learning Supervised Learning: Regression: Linear regression, logistic regression. Classification: KNN, Decision Trees, SVMs. Evaluate models using accuracy, precision, recall, F1-sco...
In modeling, we build deep models, boosting models (using XGBoost, LightGBM) and linear models (Linear Regression) and build a multi-layer stacking system to ensemble different models together. As we all know, the distribution of the training data and test data are quite different, so we ...
# Drop live projects ks = ks.query('state != "live"') # Add outcome column, "successful" == 1, others are 0 ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int)) # Timestamp features ks = ks.assign(hour=ks.launched.dt.hour, ...
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from sklearn.linear_model import LogisticRegression, SGDClassifier # 方差为1,均值为0 ss = StandardScaler() X_train = ss.fit_transform(X_train) X_test = ss.fit_transform(X_test) lr = LogisticRegression() lr.fit(X_train, y_train) lr_y_predict = lr.predict(X_test) sgdc = SGDClassifier...
test = pd.read_csv("/home/zxy/PycharmProjects/Kagglemarsggbo/data/test.csv") #Exploratory Visualization #去掉离群点,inplace代表直接在原来的数据修改,不创建新数据 train.drop(train[(train["GrLivArea"]>4000)&(train["SalePrice"]<300000)].index, inplace=True) ...
Baseline model: Linear Regression 3. American Express - Default Prediction 3-(1). XGBoost Tutorial at AMEX Competition | Github | Kaggle(kr) | Kaggle(en) -by Chris Deotte Step 1. Load Libraries Step 2. Load Dataset Step 3. Feature Engineering Step 4. Train XGB Step 5. Save OOF ...