pythonCopy codefrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import mean_squared_error# 特征选择X = df[['price', 'production_year']]y = df['sales']# 划分训练集和测试集X_train, X_test, y_train, y_test = train_...
X_train = pd.DataFrame(scaler.fit_transform(dataset_train), columns=dataset_train.columns, index=dataset_train.index) # Random shuffle training data X_train.sample(frac=1) X_test = pd.DataFrame(scaler.transform(dataset_test), columns=...
jinlist_1:sht_3[int(i),int(j)].color=(255,25,0)f()list_1=[]foriinrange(30):forjinr...
train,test=data[:train_size],data[train_size:]# 创建数据集函数defcreate_dataset(dataset,look_back=1):X,Y=[],[]foriinrange(len(dataset)-look_back-1):a=dataset[i:(i+look_back),0]X.append(a)Y.append(dataset[i+look_back,0])returnnp.array(X),np.array(Y)look_back=1X_train,Y...
train_labels[:20] Out6: 代码语言:txt AI代码解释 array([ 3, 4, 3, 4, 4, 4, 4, 3, 3, 16, 3, 3, 4, 4, 19, 8, 16, 3, 3, 21], dtype=int64) In 7: 代码语言:txt AI代码解释 test_labels[:20] Out7: 代码语言:txt ...
Python train_test_split函数实现教程 1. 整体流程 在教会小白如何实现"python train_test_split函数"之前,我们先来看一下这个过程的整体流程。下面是一个简单的流程表格: 接下来我们将逐步介绍每一个步骤,并给出相应的代码示例。 2. 操作步骤 2.1 导入必要的库 ...
train_test_split()是sklearn.model_selection中的分离器函数,⽤于将数组或矩阵划分为训练集和测试集,函数样式为: X_train, X_test, y_train, y_test = train_test_split(train_data, train_target, test_size, random_state,shuffle) 参数解释:train_data:待划分的样本数据train_target:待划分的样本数据...
: # 输出信息级别}# 创建 LightGBM 数据集lgb_train = lgb.Dataset(train_x, label=train_y)# 训练模型lgb_model = lgb.train(params, lgb_train, num_boost_round=100)# 在测试数据上预测predictions = lgb_model.predict(test_x)# 将预测结果转换为二元值predictions = [int(i > 0.5) for i in ...
创建名为 PyTrainTestSplit 的存储过程,以将 nyctaxi_sample 表中的数据划分为两部分:nyctaxi_sample_training 和 nyctaxi_sample_testing。 运行以下代码来创建它: SQL DROPPROCEDUREIFEXISTSPyTrainTestSplit; GOCREATEPROCEDURE[dbo].[PyTrainTestSplit] (@pctint)ASDROPTABLEIFEXISTSdbo.nyctaxi_sample_trainin...
dataset = pd.get_dummies(df, columns = ['sex', 'cp','fbs','restecg','exang', 'slope','ca', 'thal'])from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerstandardScaler = StandardScaler()columns_to_scale = ['age', 'trestbps', 'chol', ...