y_train = data[0].values X_train = data.drop(0, axis=1).values X_train = pd.DataFrame(np.delete(X_train, -5, axis=1)) features_name = ['a', 'b', 'c'] rf = RandomForestClassifier(n_estimators=200, oob_score=True) rf.fit(X_train, y_train) features_imp = rf.feature_im...
# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]# 或者: from sklearn.ensemble.RandomForestClassifier importoob_score[as 别名]model = RandomForestClassifier(n_jobs=6)ifargs.CV: parameters = {'n_estimators': [150,175,200],'oob_score': [True,False]}fromsklearn...
data_fb.drop('device',axis=1,inplace=True) The data is collected by two different devices apple watch and Fitbit, therefore needs to be separate. So separating the data based on the device. The data has a categorical variable that is needed to be encoded before the data is processed for...
每棵树，都能根据 oob 的 sample 得到一个预测结果，这些预测结果的平均值。
train, test = df[df['is_train']==True], df[df['is_train']==False] features = df.columns[:4] clf = RandomForestClassifier(n_jobs=2) y, _ = pd.factorize(train['species']) clf.fit(train[features], y) preds = iris.target_names[clf.predict(test[features])] ...
resting blood pressure (in mm Hg on admission to the hospital)grid_3x3cholsort serum cholestoral in mg/dlgrid_3x3fbssort (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)grid_3x3restecgsort resting electrocardiographic resultsgrid_3x3thalachsort maximum heart rate achievedgrid_3x3...
What is in question is not the retrieval of an absolute, fixed or "true" meaning that can be read off and checked for accuracy, or some timeless relationship of the text to the world. (43) ___ Such background material inevitably reflects who we are. (44) ___. This...