df = pd.concat([df, encoded_df], axis=1).drop('categorical_feature', axis=1)5. 模型构建与评估 模型构建与评估是数据分析的核心部分，涉及选择合适的算法并训练模型，以及使用交叉验证等方法评估模型性能。from sklearn.model_selection import train_test_split, cross_val_score from sklearn.linear_mode...
encoded_spicies=pd.get_dummies(categorical_data['species'])encoded_island=pd.get_dummies(categorical_data['island'])encoded_sex=pd.get_dummies(categorical_data['sex'])categorical_data=categorical_data.join(encoded_spicies)categorical_data=categorical_data.join(encoded_island)categorical_data=categorical_...
#familySize df_data['familySize'] = df_data['SibSp'] + df_data['Parch'] + 1 #isAlone df_data['isAlone'] = (df_data['familySize'] == 1).astype('category') #singleFare df_data['singleFare'] = df_data['Fare']/df_data['familySize'] # nameLen df_data['nameLen'] = df...
importnumpyasnpfromsklearnimportpreprocessingasppX_train=np.array([[1.,-5.,8.],[2.,-3.,0.],[0.,-1.,1.]])scaler=pp.MinMaxScaler().fit(X_train)# 默认数据压缩范围为[0,1]scaler# MinMaxScaler(copy=True, feature_range=(0, 1))scaler.transform(X_train)# array([[0.5 , 0. , 1....
compile(optimizer="rmsprop", loss="categorical_crossentropy",metrics=["accuracy", keras_metrics.precision(), keras_metrics.recall()])model.summary()returnmodel训练结果如下：___Layer (type) OutputShapeParam#===embedding_1 (Embedding) (None, 100, 100) 901300___...
训练数据用来分析,并训练一个分类模型(Classification Model)。使用分类模型是因为目标变量是类别数据(Categorical Data),即存活和死亡。 test.csv可以称作样本外数据(out-of-sample data)或测试数据,测试数据中只有特征变量而没有目标变量。在本例中用我们训练的模型来预测结果,并上传到kaggle评估模型的...
from tflearn.data_utils import to_categorical from sklearn.model_selection import train_test_split import sys import pandas as pd from pandas import Series,DataFrame import matplotlib.pyplot as plt data_train= pd.read_csv("feature_with_dnn_todo2.dat") ...
进行分箱操作后得到得值是字符串,还需要进行Encoding categorical features 五、one-hot Encoding / Encoding categorical features 1 2 3 4 5 pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False) dummy_na=False # 是否把 missing value...
.Dataset(X_train,label=y_train,categorical_feature=cat_features)test_data=lgb.Dataset(X_test,label=y_test,reference=train_data)# 设置参数params={'objective':'binary','metric':'binary_logloss','boosting_type':'gbdt'}# 训练模型lgb_model=lgb.train(params,train_data,valid_sets=[test_data])...
我们将使用它们来构建模型并预测Item_Outlet_Sales值。由于最终数据feature_matrix具有多个分类特征,因此我决定使用CatBoost算法。它可以直接使用分类特征,并且本质上是可扩展的。有关CatBoost的更多内容可阅读这篇文章: https://www.analyticsvidhya.com/blog/2017/08/catboost-automated-categorical-data/。