PranjalDureja0002

from sklearn.metrics import roc_curve, auc
best_depth=dt_grid.best_params_['max_depth']
best_samples=dt_grid.best_params_['min_samples_split']
dt_1 = DecisionTreeClassifier(max_depth=best_depth,min_samples_split=best_samples)

dt_1.fit(X_train, y_train)
# roc_auc_score(y_true, y_score) the 2nd parameter should be probability estimates of the positive class
# not the predicted outputs

y_train_pred = pred_func(dt_1,X_train)

PranjalDureja0002

+---------------+-----------------+----------------+----------------+---------------+
|     Model     | Train AUC Score | Test AUC Score | Train F1 Score | Test F1 Score |
+---------------+-----------------+----------------+----------------+---------------+
| Decision_Tree |      0.9967     |     0.9909     |    0.99314     |     0.9771    |
+---------------+-----------------+----------------+----------------+---------------+

PranjalDureja0002

# A parameter grid for XGBoost
parameters = {    
    'n_estimators': [100,500,1000],
    'learning_rate': [0.1, 0.01, 0.05]
}

xgb = XGBClassifier(objective='binary:logistic',
                    silent=True, nthread=4)

xg_grid = GridSearchCV(xgb, param_grid=parameters, n_jobs=-1, verbose=1,scoring='f1_macro',cv=3,return_train_score=True)

PranjalDureja0002

training score: 0.9998006310038143
testing score: 0.990791808142236

PranjalDureja0002

feature_important = xg1.get_booster().get_score(importance_type='weight')
keys = list(feature_important.keys())
values = list(feature_important.values())

data = pd.DataFrame(data=values, index=keys, columns=["score"]).sort_values(by = "score", ascending=False)
data.head(20)

PranjalDureja0002

provider_count	14960
State	5550
attend_physician_count	4258
County	3218
OPAnnualReimbursementAmt	816
total_diff_amount	800
ClmDiagnosisCode_1_count	713
OPAnnualDeductibleAmt	668
InscClaimAmtReimbursed	634

PranjalDureja0002

s1 = data['score']
s_s1 = sum(s1.tolist())
s_s1

plt.style.use('fivethirtyeight')
ax=data.head(20).plot(kind = 'barh' , color = 'red')
for p in ax.patches:
        percentage = '{:.1f}%'.format(100 * p.get_width()/s_s1) 
        x = p.get_x() + p.get_width() - 0.5
        y = p.get_y() + p.get_height()

PranjalDureja0002

+---------+-----------------+----------------+----------------+---------------+
|  Model  | Train AUC Score | Test AUC Score | Train F1 Score | Test F1 Score |
+---------+-----------------+----------------+----------------+---------------+
| XgBoost |     0.99938     |    0.99855     |     0.9998     |    0.990791   |
+---------+-----------------+----------------+----------------+---------------+

PranjalDureja0002

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
x_cfl=XGBClassifier(n_estimators=1000,nthread=-1)
x_1=XGBClassifier(n_estimators=500,nthread=-1)
x_2=XGBClassifier(n_estimators=500,nthread=-1)

x_3 = DecisionTreeClassifier(max_depth=best_depth,min_samples_split=best_samples,class_weight='balanced')
x_4 = LogisticRegression(class_weight='balanced')
s_clf = StackingClassifier(classifiers=[x_1,x_2,x_3,x_4],meta_classifier=x_cfl)
s_clf.fit(X_train,y_train)

PranjalDureja0002

+---------------------+-----------------+----------------+----------------+---------------+
|        Model        | Train AUC Score | Test AUC Score | Train F1 Score | Test F1 Score |
+---------------------+-----------------+----------------+----------------+---------------+
| Stacking_Classifier |     0.99537     |     0.9902     |    0.99429     |    0.98759    |
+---------------------+-----------------+----------------+----------------+---------------+