Pandas Series是一维数组状结构,带有轴标签;Pandas DataFrame是二维表格型结构,包含多个可能不同类型的列,每个列是一个Series。 1. 维度特征:Series是单一维度的数据容器,表现形式为带索引的一列值;DataFrame是二维结构,由多个Series组成列,并共享行索引。2. 数据结构:Series的创建方式为pd.Series(data
DataFrame(weights, index=target_labels, columns=filters)Visualizing the weights:plt.figure(figsize=(15, 10)) # focus on annotated filters only sns.clustermap(weight_df[[i for i in weight_df.columns if not i.startswith("filter")]], cmap=sns.diverging_palette(145, 10, s=60, as_cmap=...
import pandas as pd Assuming df is your DataFrame correlation_matrix = df.corr() print(correlation_matrix) Variance Inflation Factor (VIF) The VIF quantifies how much the variance of a regression coefficient is inflated due to multicollinearity. A VIF value greater than 10 is commonly considered...
#Load Dataset: X_Data, Y_Data#X_Data = Pandas DataFrame#Y_Data = Numpy Array or ListX_data,Y_data=explainx.dataset_heloc() Split dataset into training & testing. X_train,X_test,Y_train,Y_test=train_test_split(X_data,Y_data,test_size=0.3,random_state=0) ...
#Load Dataset: X_Data, Y_Data#X_Data = Pandas DataFrame#Y_Data = Numpy Array or ListX_data,Y_data=explainx.dataset_heloc() Split dataset into training & testing. X_train,X_test,Y_train,Y_test=train_test_split(X_data,Y_data,test_size=0.3,random_state=0) ...
(in this example it is simply converting predictions to a string)model_predictions=pd.DataFrame(predictions.astype(str),columns=[y_train.name],index=y_train.index)# use Explainer to explain model outputexplainer=Explainer(X=X_train,model_predictions=model_predictions,type="classification")explainer....