Explain Model with SHAP Value

import pandas as pd
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
df = pd.read_csv('/winequality-red.csv') # Load the data
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestRegressor
# The target variable is 'quality'.
Y = df['quality']
X =  df[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar','chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density','pH', 'sulphates', 'alcohol']]
# Split the data into train and test data:
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)
# Build the model with the random forest regression algorithm:
model = RandomForestRegressor(max_depth=6, random_state=0, n_estimators=10)
model.fit(X_train, Y_train)

Plot Feature Importance with Blue Bar Charts

1	import shap
2	shap_values = shap.TreeExplainer(model).shap_values(X_train)
3	shap.summary_plot(shap_values, X_train, plot_type="bar")

Plot the Red&Blue Feature Importance Plot

1	shap.summary_plot(shap_values, X_train)

Partial Dependence Plot

The partial dependence plot shows the marginal effect one or two features have on the predicted outcome of a machine learning model

1	shap.dependence_plot(“alcohol”, shap_values, X_train)

View the original article on Mediumn:
Explain Your Model with the SHAP Values

1	import pandas as pd
2	import numpy as np
3	np.random.seed(0)
4	import matplotlib.pyplot as plt
5	df = pd.read_csv('/winequality-red.csv') # Load the data
6	from sklearn.model_selection import train_test_split
7	from sklearn import preprocessing
8	from sklearn.ensemble import RandomForestRegressor
9	# The target variable is 'quality'.
10	Y = df['quality']
11	X = df[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar','chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density','pH', 'sulphates', 'alcohol']]
12	# Split the data into train and test data:
13	X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)
14	# Build the model with the random forest regression algorithm:
15	model = RandomForestRegressor(max_depth=6, random_state=0, n_estimators=10)
16	model.fit(X_train, Y_train)

Import related libriaires and define variables

Plot Feature Importance with Blue Bar Charts

Plot the Red&Blue Feature Importance Plot

Partial Dependence Plot