I've trained a simple Neural net (scikit learn's MLPClassifier) in order to perform binary classification on some data (the titanic dummy problem on kaggle).

I know that standardizing data prior to using it to train neural nets is supposed to make training faster but I didn't expect it to change the results (it scores around 65% average on non-standardized data vs around 80% for standardized data)

# comment the following code to see the difference
scaler = StandardScaler()
scaler.fit(X_train)

X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

I noticed that the results of both approaches seem to converge when I train the neural net using full batch gradient descent (the default is to use mini batches with stochastic gradient descent).

see the notebook here (look at parapgraph [8])

Are these discrepancies solely due to the stochastic nature of SGD?

I've trained a simple Neural net (scikit learn's MLPClassifier) in order to perform binary classification on some data (the titanic dummy problem on kaggle).

I know that standardizing data prior to using it to train neural nets is supposed to make training faster but I didn't expect it to change the results (it scores around 65% accuracy on non-standardized data vs around 80% for standardized data (local test set) )

# comment the following code to see the difference
scaler = StandardScaler()
scaler.fit(X_train)

X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

I noticed that the results of both approaches seem to converge when I train the neural net using full batch gradient descent (the default is to use mini batches with stochastic gradient descent).

see the notebook here (look at parapgraph [8])

Question: Are these discrepancies solely due to the stochastic nature of SGD?

I've trained a simple Neural net (scikit learn's MLPClassifier) in order to perform binary classification on some data (the titanic dummy problem on kaggle).

I know that standardizing data prior to using it to train neural nets is supposed to make training faster but I didn't expect it to change the results (it scores around 65% average on non-standardized data vs around 80% for standardized data)

I noticed that the results of both approaches seem to converge when I train the neural net using full batch gradient descent (the default is to use stochastic gradient descent).

see the notebook here

Are these discrepancies solely due to the stochastic nature of SGD?

I've trained a simple Neural net (scikit learn's MLPClassifier) in order to perform binary classification on some data (the titanic dummy problem on kaggle).

I know that standardizing data prior to using it to train neural nets is supposed to make training faster but I didn't expect it to change the results (it scores around 65% average on non-standardized data vs around 80% for standardized data)

# comment the following code to see the difference
scaler = StandardScaler()
scaler.fit(X_train)

X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

I noticed that the results of both approaches seem to converge when I train the neural net using full batch gradient descent (the default is to use mini batches with stochastic gradient descent).

see the notebook here (look at parapgraph [8])

Are these discrepancies solely due to the stochastic nature of SGD?