728x90 반응형 MachineLearning19 랜덤포레스트 from sklearn.model_selection import train_test_split from bayes_opt import BayesianOptimization rf_parameter_bounds = { 'max_depth' : (5,30), 'n_estimators': (10,100), 'min_samples_split': (2,10), 'min_samples_leaf': (1,4) } def NMAE(true, pred): score = np.sum(np.abs(true-pred)) / np.sum(true) return score def rf_bo(max_depth,n_estimators,min_samples_split, min_samples_leaf): rf_params = { 'max.. 2022. 8. 31. 파일생성 submission = pd.read_csv('submission.csv') submission['count']=linear_prediction submission.to_csv('linear.csv', index = False) 2022. 8. 31. 단순 선형회귀 분석 from sklearn.model_selection import KFold from sklearn.linear_model import LinearRegression linear_model_result = [] kf = KFold(n_splits = 5) for idx, (trn_idx, val_idx) in enumerate(kf.split(train_x)): train_x = np.array(train_x) trn_x = train_x[trn_idx] val_x = train_x[val_idx] trn_y = train_y[trn_idx] val_y = train_y[val_idx] linear_model = LinearRegression() linear_model.fit(trn_x, trn_y) li.. 2022. 8. 31. 특정 상관계수 이하 제거 및 x,y 분리 drop_list=[] corr = train.corr()['count'] drop_list.extend(corr[abs(corr) 2022. 8. 31. 이전 1 2 3 4 5 다음 728x90 반응형