Is this a data issue, or a model issue? A Keras binary classification model
$begingroup$
I've been trying to create a binary classification model that predicts wether there will be a train delay based on the train and time. Here is a link to the data
The issue I'm having is that my accuracy goes to 94.07 in the first 5 epochs. Meanwhile, my class prediction will always be 0 and never 1.
From what I understand, this is "Accuracy Paradox". A symptom of Class Imbalance. To combat this, I implemented Kfold.
kfold = StratifiedKFold(n_splits=10,shuffle=True)
cvs_scores =
for train,test in kfold.split(X,Y):
history = model.fit(X[train],Y[train],epochs=50,batch_size=15, shuffle = False, verbose = 1)
scores = model.evaluate(X[test],Y[test],verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1],scores[1]*100))
cvs_scores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvs_scores),numpy.std(cvs_scores)))
No luck. Still had the same issue as before.
Here is how I import my data:
raw_data = pd.read_csv('MTA_DELAY_DATA_DUMP - Sheet1.csv')
X = raw_data.iloc[1:-2,0:2].dropna().values
Y = raw_data.iloc[1:-2,2:3].dropna().astype(int).values
My Model:
model = Sequential()
model.add(Dense(32, kernel_initializer='uniform', activation='relu',input_dim =2))
model.add(Dense(16, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001),metrics=['accuracy'])
history = model.fit(X,Y,epochs=150,batch_size=15, shuffle = False, verbose = 1)
I tried assigning class weights to balance the data out. Even manually deleting 0's in the data, but nothing seems to result in accurate predictions. Am I doing something wrong in the model, or is this simply data that cannot be utilized by machine learning?
machine-learning deep-learning keras dataset data-cleaning
asked 2 mins ago
PavlovsCatPavlovsCat
1
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I've been trying to create a binary classification model that predicts wether there will be a train delay based on the train and time. Here is a link to the data
The issue I'm having is that my accuracy goes to 94.07 in the first 5 epochs. Meanwhile, my class prediction will always be 0 and never 1.
From what I understand, this is "Accuracy Paradox". A symptom of Class Imbalance. To combat this, I implemented Kfold.
kfold = StratifiedKFold(n_splits=10,shuffle=True)
cvs_scores =
for train,test in kfold.split(X,Y):
history = model.fit(X[train],Y[train],epochs=50,batch_size=15, shuffle = False, verbose = 1)
scores = model.evaluate(X[test],Y[test],verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1],scores[1]*100))
cvs_scores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvs_scores),numpy.std(cvs_scores)))
No luck. Still had the same issue as before.
Here is how I import my data:
raw_data = pd.read_csv('MTA_DELAY_DATA_DUMP - Sheet1.csv')
X = raw_data.iloc[1:-2,0:2].dropna().values
Y = raw_data.iloc[1:-2,2:3].dropna().astype(int).values
My Model:
model = Sequential()
model.add(Dense(32, kernel_initializer='uniform', activation='relu',input_dim =2))
model.add(Dense(16, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001),metrics=['accuracy'])
history = model.fit(X,Y,epochs=150,batch_size=15, shuffle = False, verbose = 1)
I tried assigning class weights to balance the data out. Even manually deleting 0's in the data, but nothing seems to result in accurate predictions. Am I doing something wrong in the model, or is this simply data that cannot be utilized by machine learning?
machine-learning deep-learning keras dataset data-cleaning
asked 2 mins ago
PavlovsCatPavlovsCat
1
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I've been trying to create a binary classification model that predicts wether there will be a train delay based on the train and time. Here is a link to the data
The issue I'm having is that my accuracy goes to 94.07 in the first 5 epochs. Meanwhile, my class prediction will always be 0 and never 1.
From what I understand, this is "Accuracy Paradox". A symptom of Class Imbalance. To combat this, I implemented Kfold.
kfold = StratifiedKFold(n_splits=10,shuffle=True)
cvs_scores =
for train,test in kfold.split(X,Y):
history = model.fit(X[train],Y[train],epochs=50,batch_size=15, shuffle = False, verbose = 1)
scores = model.evaluate(X[test],Y[test],verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1],scores[1]*100))
cvs_scores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvs_scores),numpy.std(cvs_scores)))
No luck. Still had the same issue as before.
Here is how I import my data:
raw_data = pd.read_csv('MTA_DELAY_DATA_DUMP - Sheet1.csv')
X = raw_data.iloc[1:-2,0:2].dropna().values
Y = raw_data.iloc[1:-2,2:3].dropna().astype(int).values
My Model:
model = Sequential()
model.add(Dense(32, kernel_initializer='uniform', activation='relu',input_dim =2))
model.add(Dense(16, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001),metrics=['accuracy'])
history = model.fit(X,Y,epochs=150,batch_size=15, shuffle = False, verbose = 1)
I tried assigning class weights to balance the data out. Even manually deleting 0's in the data, but nothing seems to result in accurate predictions. Am I doing something wrong in the model, or is this simply data that cannot be utilized by machine learning?
machine-learning deep-learning keras dataset data-cleaning
asked 2 mins ago
PavlovsCatPavlovsCat
1
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I've been trying to create a binary classification model that predicts wether there will be a train delay based on the train and time. Here is a link to the data
The issue I'm having is that my accuracy goes to 94.07 in the first 5 epochs. Meanwhile, my class prediction will always be 0 and never 1.
From what I understand, this is "Accuracy Paradox". A symptom of Class Imbalance. To combat this, I implemented Kfold.
kfold = StratifiedKFold(n_splits=10,shuffle=True)
cvs_scores =
for train,test in kfold.split(X,Y):
history = model.fit(X[train],Y[train],epochs=50,batch_size=15, shuffle = False, verbose = 1)
scores = model.evaluate(X[test],Y[test],verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1],scores[1]*100))
cvs_scores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvs_scores),numpy.std(cvs_scores)))
No luck. Still had the same issue as before.
Here is how I import my data:
raw_data = pd.read_csv('MTA_DELAY_DATA_DUMP - Sheet1.csv')
X = raw_data.iloc[1:-2,0:2].dropna().values
Y = raw_data.iloc[1:-2,2:3].dropna().astype(int).values
My Model:
model = Sequential()
model.add(Dense(32, kernel_initializer='uniform', activation='relu',input_dim =2))
model.add(Dense(16, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001),metrics=['accuracy'])
history = model.fit(X,Y,epochs=150,batch_size=15, shuffle = False, verbose = 1)
I tried assigning class weights to balance the data out. Even manually deleting 0's in the data, but nothing seems to result in accurate predictions. Am I doing something wrong in the model, or is this simply data that cannot be utilized by machine learning?
machine-learning deep-learning keras dataset data-cleaning
machine-learning deep-learning keras dataset data-cleaning
asked 2 mins ago
PavlovsCatPavlovsCat
1
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
PavlovsCatPavlovsCat
1
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
PavlovsCatPavlovsCat
1
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
PavlovsCatPavlovsCat
1
asked 2 mins ago
PavlovsCatPavlovsCat
1
1
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
PavlovsCat is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
PavlovsCat is a new contributor. Be nice, and check out our Code of Conduct.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f49448%2fis-this-a-data-issue-or-a-model-issue-a-keras-binary-classification-model%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
PavlovsCat is a new contributor. Be nice, and check out our Code of Conduct.
PavlovsCat is a new contributor. Be nice, and check out our Code of Conduct.
PavlovsCat is a new contributor. Be nice, and check out our Code of Conduct.
PavlovsCat is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f49448%2fis-this-a-data-issue-or-a-model-issue-a-keras-binary-classification-model%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
