rfmodel = RandomForestClassifier( n_estimators=100, max_depth=5, min_samples_split=2, random_state=seed_value) rfmodel.fit(X_train, y_train) #预测测试集数据 y_train_pred = rfmodel.predict(X_train) print(预测训练集结果如下
('Decision Tree Clf', DecisionTreeClassifier(max_depth=3, min_samples_split=2, min_samples_leaf=1, random_state=42)), ('Random Forest Clf', RandomForestClassifier(n_estimators=200, max_depth=5, min_samples_split=2, random_state=42)), ('Logistic Clf', LogisticRegression(penalty='l2', ...
-max_depth:单个决策树的最大深度,默认为None(表示不限制深度)。 -random_state:随机种子值,默认为None(表示不使用随机种子)。 训练随机森林模型 首先,我们需要导入所需的库和模块。随机森林模型通常需要使用sklearn.ensemble库中的RandomForestClassifier(用于分类问题)或RandomForestRegressor(用于回归问题)模块。我们...
The BSN is developed using a constructive approach; each hypothesis is tested through a comparison experiment, with results of previous experiments guiding subsequent ones. Experiments are conducted using alearning setof 250 patients, with evaluation carried out (by 5-fold cross-validation) on 5test ...
from sklearn import datasets from sklearn.ensemble import RandomForestClassifier with mlflow.start_run(): # Train a sklearn model on the iris dataset X, y = datasets.load_iris(return_X_y=True, as_frame=True) clf = RandomForestClassifier(max_depth=7) clf.fit(X, y) # Take the first...
X, y=digits.data, digits.target#建立一个分类器或者回归器clf = RandomForestClassifier(n_estimators=20)#给定参数搜索范围:list or distributionparam_dist = {"max_depth": [3, None],#给定list"max_features": sp_randint(1, 11),#给定distribution"min_samples_split": sp_randint(2, 11),#给定dis...
The default TWS Random Forest (RF) classifier was selected with the following options: maxDepth = 20; numFeatures = 2; numTrees = 80. Classifier options were chosen considering a balance between segmentation performance and cost in processing time (due to image size). As shown ...
clf = DecisionTreeClassifier(max_depth=None, min_samples_split=2,random_state=0) scores = cross_val_score(clf, X, y) print('决策树准确率:',scores.mean()) # 随机森林 from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier(n_estimators=10,max_features=2) ...
3.2.5 Decision trees and random forests Decision Tree (DT) model is a classical ML based model which categorizes the data into various subsets to identify the potential structure, patterns and relationships among the data [63]. DT does not make any assumptions about the relationships among the...
X, y=digits.data, digits.target#元分类器meta_clf = RandomForestClassifier(n_estimators=20)#===#设置参数param_dist = {"max_depth": [3, None],"max_features": sp_randint(1, 11),"min_samples_split": sp_randint(1, 11),"min_samples_leaf": sp_randint(1, 11),"bootstrap": [True,...