random seed(123)每次运行结果一致 如果你在需要设置随机数种子的地方都设置好,那么当别人重新运行你的代码的时候就能得到完全一样的结果,复现和你一样的过程。 使用情况 三种情况: 1、在构建模型时: forest = RandomForestClassifier(n_estimators=100, random_state=0) forest.fit(X_train, y_train) 2、在生...
from sklearn.ensemble import RandomForestClassifier from sklearn.grid_search import GridSearchCV from sklearn import cross_validation, metrics import matplotlib.pylab as plt #导入数据 train = pd.read_csv('train_modified.csv') target='Disbursed' # Disbursed的值就是二元分类的输出 IDcol = 'ID' tr...
# 训练随机森林 model = RandomForestClassifier(n_trees=100, max_depth=5, min_samples_split=2, random_state=seed_value) model.fit(X_train, y_train) 4.7 打印结果 模型训练完成之后可以使用如下代码查看训练集和测试集的准确率,如果有能力小伙伴可以绘制AUC曲线等查看模型效果。 # 结果 y_train_pred...
from sklearn.model_selection import cross_val_score from sklearn.datasets import make_blobs from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import ExtraTreesClassifier from sklearn.tree import DecisionTreeClassifier X, y = make_blobs(n_samples=10000, n_features=10, centers=...
三、sklearn参数详解 classsklearn.ensemble.RandomForestClassifier(n_estimators=’warn’,criterion=’gini’,max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features=’auto’,max_leaf_nodes=None,min_impurity_decrease=0.0,min_impurity_split=None,bootstrap=True...
importnumpyasnpfromsklearn.ensembleimportRandomForestClassifierseed=0n_rows=3n_cols=90_000rng=np.random.default_rng(seed)X=rng.integers(low=0,high=2,size=(n_rows,n_cols)).astype(np.float64)y=rng.integers(low=0,high=2,size=n_rows)clf=RandomForestClassifier(n_estimators=1,random_state=se...
forest = RandomForestClassifier(n_estimators=100, random_state=0) forest.fit(X_train, y_train) 2、在生成数据集时: X, y = make_moons(n_samples=100, noise=0.25, random_state=3) 3、在拆分数据集为训练集、测试集时: X_train, X_test, y_train, y_test = train_test_split( ...
示例1: RandomForestClassifier ▲点赞 6▼ # 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]# 或者: from sklearn.ensemble.RandomForestClassifier importrandom_state[as 别名]clf = RandomForestClassifier(n_estimators=32, max_depth=40, min_samples_split=100, min_samp...
from sklearn.grid_search import RandomizedSearchCV from sklearn.pipeline import make_pipeline from scipy.stats import randint as sp_randint seed = np.random.seed(22) X_train, X_test, y_train, y_test = train_test_split(data[features], data['target']) clf = RandomForestClassifier() kbe...
開發者ID:PacktPublishing,項目名稱:Mastering-Elasticsearch-7.0,代碼行數:40,代碼來源:test_forest.py 注:本文中的sklearn.utils.validation.check_random_state方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應...