來看看 random_state 這個(gè)參數(shù) SVC(random_state=0)里有參數(shù) random_state from imblearn.over_sampling import SMOTE SMOTE(random_state=42) 里有參數(shù) random_state 上面一個(gè)是svd算法,一個(gè)是處理不平衡數(shù)據(jù)的smote算法,我都遇到了random_state這個(gè)參數(shù),那么......
...機(jī)分割訓(xùn)練集和測試集: # test_size:設(shè)置測試集的比例。random_state:可理解為種子,保證隨機(jī)唯一 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3., random_state=8) sklearn實(shí)戰(zhàn)例子: from sklearn import datasets ...
...機(jī)分割訓(xùn)練集和測試集: # test_size:設(shè)置測試集的比例。random_state:可理解為種子,保證隨機(jī)唯一 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3., random_state=8) sklearn實(shí)戰(zhàn)例子: from sklearn import datasets ...
...Frame # 生成2分類數(shù)據(jù)集 X, y = make_moons(n_samples=100, noise=0.2, random_state=1) print(X.shape) print(X[:6]) print(y.shape) print(y[:6]) df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y)) colors = {0:...
... =cross_validation.train_test_split(train_data,train_target,test_size=0.3, random_state=0) 參數(shù)解釋: train_data:被劃分的樣本特征集 train_target:被劃分的樣本標(biāo)簽 test_size:如果是浮點(diǎn)數(shù),在0-1之間,表示樣本占比;如果是整數(shù)的話就是樣本的數(shù)...
...集占比x_train,x_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=90)lr=LogisticRegression(max_iter=3000)clm=lr.fit(x_train,y_train)print(對測試集的預(yù)測結(jié)果:)#輸出預(yù)測結(jié)果、預(yù)測結(jié)果的結(jié)構(gòu)類型及尺寸result=clm.p...
ChatGPT和Sora等AI大模型應(yīng)用,將AI大模型和算力需求的熱度不斷帶上新的臺階。哪里可以獲得...
大模型的訓(xùn)練用4090是不合適的,但推理(inference/serving)用4090不能說合適,...
圖示為GPU性能排行榜,我們可以看到所有GPU的原始相關(guān)性能圖表。同時(shí)根據(jù)訓(xùn)練、推理能力由高到低做了...