betterdatascience

lime_exp.as_list()

betterdatascience

shap.summary_plot(shap_values, X)

betterdatascience

import shap
shap.initjs()

shap_explainer = shap.TreeExplainer(model)
shap_values = shap_explainer.shap_values(X)

shap.force_plot(shap_explainer.expected_value, shap_values[1, :], test_1)

betterdatascience

lime_exp.as_list()

betterdatascience

lime_exp.predict_proba

betterdatascience

import lime 
from lime import lime_tabular

lime_explainer = lime_tabular.LimeTabularExplainer(
    training_data=np.array(X_train),
    feature_names=X_train.columns,
    class_names=['bad', 'good'],
    mode='classification'
)

betterdatascience

from xgboost import XGBClassifier

model = XGBClassifier()
model.fit(X_train, y_train)

test_1 = X_test.iloc[1]

betterdatascience

from sklearn.model_selection import train_test_split

X = wine.drop('quality', axis=1)
y = wine['quality']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

betterdatascience

import numpy as np
import pandas as pd

wine = pd.read_csv('wine.csv')
wine.head()

betterdatascience

def generate_color(magnitude):
    if magnitude <= 5:
        c_outline, c_fill = '#ffda79', '#ffda79'
        m_opacity, f_opacity = 0.2, 0.1
    else:
        c_outline, c_fill = '#c0392b', '#e74c3c'
        m_opacity, f_opacity = 1, 1
    return c_outline, c_fill, m_opacity, f_opacity

def generate_popup(magnitude, depth):