Audio Model Selection

AudioModel.py

# algorithm 1 ------------------------------------------------------------------
print(" Naive Bayes ... ")

from sklearn import naive_bayes

classifier = naive_bayes.GaussianNB()
nb_model = classifier.fit(X, Y)
prediction = nb_model.predict(X_test)

print(" accuracy = ", accuracy_score(Y_test, prediction)) 
print(" f1_score = ", f1_score(Y_test, prediction))
print(confusion_matrix(Y_test, prediction))

# algorithm 2 ------------------------------------------------------------------
print(" Random Forest ... ")

from sklearn.ensemble import RandomForestClassifier

classifier = RandomForestClassifier()
rf_model = classifier.fit(X, Y)
prediction = rf_model.predict(X_test)

print(" accuracy = ", accuracy_score(Y_test, prediction))
print(" f1_score = ", f1_score(Y_test, prediction))
print(confusion_matrix(Y_test, prediction))

# algorithm 3 ------------------------------------------------------------------
print(" Gradient Boosting ... ")

from sklearn.ensemble import GradientBoostingClassifier as gbc

classifier = gbc()
gbc_model = classifier.fit(X, Y)
prediction = gbc_model.predict(X_test)

print(" accuracy = ", accuracy_score(Y_test, prediction))
print(" f1_score = ", f1_score(Y_test, prediction))
print(confusion_matrix(Y_test, prediction))

# algorithm 4 ------------------------------------------------------------------
print(" XGBOOST ... ")

from xgboost import XGBClassifier

xgb_model = XGBClassifier()
xgb_model.fit(X, Y)
prediction = xgb_model.predict(X_test)

print(" accuracy = ", accuracy_score(Y_test, prediction))
print(" f1_score = ", f1_score(Y_test, prediction))
print(confusion_matrix(Y_test, prediction))