Question: I have the following dataframe: import pandas as pd import random import xgboost import shap foo = pd.DataFrame({'id':[1,2,3,4,5,6,7,8,9,10], 'var1':random.sample(range(1, 100), 10), 'var2':random.sample(range(1, 100), 10), 'var3':random.sample(range(1, 100), 10), 'class': ['a','a','a','a','a','b','b','c','c','c']})  I want to run a classification algorithm to predict the 3 classes. So I split my dataset into a training and testing set and I ran an xgboost classification cl_cols = foo.filter(regex='var').columns X_train, X_test, y_train, y_test = train_test_split(foo[cl_cols], foo[['class']], test_size=0.33, random_state=42) model = xgboost.XGBClassifier(objective="binary:logistic") model.fit(X_train, y_train)  Now I would like to get the mean SHAP values for each class, instead of the mean from the absolute SHAP values generated from this code: shap_values = shap.TreeExplainer(model).shap_values(X_test) shap.summary_plot(shap_values, X_test)   Also, the plot labels the class as 0,1,2. How can I know to which class the 0,1 & 2 from the original correspond? Because this code: shap.summary_plot(shap_values, X_test, class_names= ['a', 'b', 'c'])  gives  and this code: shap.summary_plot(shap_values, X_test, class_names= ['b', 'c', 'a'])  gives  So I am not sure about the legend anymore. Any ideas?

Answer: By doing some research and with the help of this post and @Alessandro Nesti 's answer, here is my solution: foo = pd.DataFrame({'id':[1,2,3,4,5,6,7,8,9,10], 'var1':random.sample(range(1, 100), 10), 'var2':random.sample(range(1, 100), 10), 'var3':random.sample(range(1, 100), 10), 'class': ['a','a','a','a','a','b','b','c','c','c']}) cl_cols = foo.filter(regex='var').columns X_train, X_test, y_train, y_test = train_test_split(foo[cl_cols], foo[['class']], test_size=0.33, random_state=42) model = xgboost.XGBClassifier(objective="multi:softmax") model.fit(X_train, y_train) def get_ABS_SHAP(df_shap,df): #import matplotlib as plt # Make a copy of the input data shap_v = pd.DataFrame(df_shap) feature_list = df.columns shap_v.columns = feature_list df_v = df.copy().reset_index().drop('index',axis=1) # Determine the correlation in order to plot with different colors corr_list = list() for i in feature_list: b = np.corrcoef(shap_v[i],df_v[i])[1][0] corr_list.append(b) corr_df = pd.concat([pd.Series(feature_list),pd.Series(corr_list)],axis=1).fillna(0) # Make a data frame. Column 1 is the feature, and Column 2 is the correlation coefficient corr_df.columns = ['Variable','Corr'] corr_df['Sign'] = np.where(corr_df['Corr']>0,'red','blue') shap_abs = np.abs(shap_v) k=pd.DataFrame(shap_abs.mean()).reset_index() k.columns = ['Variable','SHAP_abs'] k2 = k.merge(corr_df,left_on = 'Variable',right_on='Variable',how='inner') k2 = k2.sort_values(by='SHAP_abs',ascending = True) k2_f = k2[['Variable', 'SHAP_abs', 'Corr']] k2_f['SHAP_abs'] = k2_f['SHAP_abs'] * np.sign(k2_f['Corr']) k2_f.drop(columns='Corr', inplace=True) k2_f.rename(columns={'SHAP_abs': 'SHAP'}, inplace=True) return k2_f foo_all = pd.DataFrame() for k,v in list(enumerate(model.classes_)): foo = get_ABS_SHAP(shap_values[k], X_test) foo['class'] = v foo_all = pd.concat([foo_all,foo]) import plotly_express as px px.bar(foo_all,x='SHAP', y='Variable', color='class')  which results in