Hi,

I’m pretty new to ML.

For a school project, I try to optimize the tpr of my confusion matrix, but i use a Sequential model from keras and they don’t have the predict_proba of scikitlearn. How could i have the probabilities to modify the threshold ?

```
def perceptron(X,y):
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = km.Sequential()
model.add(kl.Dense(30, activation='sigmoid' , input_dim=X.shape[1]))
model.add(kl.Dense(30, activation='sigmoid'))
model.add(kl.Dense(8, activation='sigmoid'))
model.add(kl.Dense(5, activation='sigmoid'))
model.add(kl.Dense(1, activation='sigmoid'))
model.compile(optimizer='Adam', loss='mae')
history = model.fit(X_train, y_train,
batch_size=10,
epochs=100,
validation_split=0.2,
verbose=0) # % of data being used for val_loss evaluation
ev = model.evaluate(X_test, y_test)
return y_test,X_test,history,model
```

```
def classification(y_test,X_test,history,model,aff):
y_pred = model.predict(X_test)
for i in range(len(y_pred)):
if y_pred[i]<0.5:
y_pred[i]=0
if y_pred[i]>0.5:
y_pred[i]=1
threshold = 0.5
probabilities = tf.nn.softmax(X_test).numpy()
pred = np.argmax(probabilities, axis=1)
print(probabilities)
print(pred)
class_report = classification_report(y_test, y_pred) # Additional evaluation metrics
# Calculate confusion matrix
conf_matrix = confusion_matrix(y_test, y_pred,labels=[0,1])
```

Instead of the y_pred = model.predict(X_test), what can i do ?

Thanks