SkikitLearn learning curve strongly dependent on batch size of MLPClassifier ??? Or: how to diagnose bias/ variance for NN?How to assess a neural network MLPClassifier in ScikitLearn (concerning bias/ variance/ cross validation)?Python sklearn: why must I set up a new estimator to plot a learning curve?Learning curve (high bias / high variance) why the testing learning curve gets flatConfusing example of nested cross validation in scikit-learnHow to get the train_scores to plot a learning curve without using the learning_curve fuction fo scikitlearn?Must accuracy increase after every epoch?Plotting Learning Curves [Scitkit Learn] - How to set x-axis values/labels?Validation and training accuracy high in the first epoch [Keras]Nest cross validation for predictions using groupsWhy is a learning curve necessary to determine if a neural network has high bias or variance?Interpretation of learning curve in ScikitLearn concerning epochs
Mechanical puzzle ID: Ring, barbell, and four-holed panel
Is there an English equivalent for "Les carottes sont cuites", while keeping the vegetable reference?
Why does FFmpeg choose 10+20+20 ms instead of an even 16 ms for 60 fps GIF images?
Intel 8080-based home computers
Cauchy reals and Dedekind reals satisfy "the same mathematical theorems"
What made Windows ME so crash-prone?
What happens if there is no space for entry stamp in the passport for US visa?
Can I remove the doors before installing a sliding patio doors frame?
Do aircraft cabins have suspension?
Does the Intel 8085 CPU use real memory addresses?
Why did Steve Rogers choose this character in Endgame?
Why does "git status" show I'm on the master branch and "git branch" does not?
Accidentally deleted python and yum is not working in centos7
Is it rude to refer to janitors as 'floor people'?
If SWIFT is headquartered in Europe, why does the EU need to create a SWIFT alternative to be able to do transactions with Iran?
Why should I cook the flour first when making bechamel sauce?
Did Voldemort kill his father before finding out about Horcruxes?
Can a pizza stone be fixed after soap has been used to clean it?
What are the arguments for California’s nonpartisan blanket (jungle) primaries?
Why did Spider-Man take a detour to Dorset?
What exactly is a Hadouken?
How to remove the first colon ':' from a timestamp?
Can an American visitor in Canada get critical medications sent from home?
Bracket symbol added to G clef
SkikitLearn learning curve strongly dependent on batch size of MLPClassifier ??? Or: how to diagnose bias/ variance for NN?
How to assess a neural network MLPClassifier in ScikitLearn (concerning bias/ variance/ cross validation)?Python sklearn: why must I set up a new estimator to plot a learning curve?Learning curve (high bias / high variance) why the testing learning curve gets flatConfusing example of nested cross validation in scikit-learnHow to get the train_scores to plot a learning curve without using the learning_curve fuction fo scikitlearn?Must accuracy increase after every epoch?Plotting Learning Curves [Scitkit Learn] - How to set x-axis values/labels?Validation and training accuracy high in the first epoch [Keras]Nest cross validation for predictions using groupsWhy is a learning curve necessary to determine if a neural network has high bias or variance?Interpretation of learning curve in ScikitLearn concerning epochs
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I am currently working on a classification problem with two classes in ScikitLearn with the solver adam and activation relu. To explore if my classifier suffers from high bias or high variance, I plotted the learning curve with Scikitlearns build-in function:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
I am using a Group-K_Fold crossvalidation with 8 splits.
However, I found that my learning curve is strongly dependent on the batch size of my classifier:
https://imgur.com/a/FOaWKN1
Is it supposed to be like this? I thought learning curves are tackling the accuracy scores dependent on the portion of training data independent from any batches/ epochs? Can I actually use this build-in function for batch methods? If yes, which batch size should I choose (full batch or batch size= number of training examples or something in between) and what diagnosis do I get from this? Or how do you usually diagnose bias/ variance problems of a neural network classifier?
Help would be really appreciated!
scikit-learn neural-network mlp
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
add a comment |
I am currently working on a classification problem with two classes in ScikitLearn with the solver adam and activation relu. To explore if my classifier suffers from high bias or high variance, I plotted the learning curve with Scikitlearns build-in function:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
I am using a Group-K_Fold crossvalidation with 8 splits.
However, I found that my learning curve is strongly dependent on the batch size of my classifier:
https://imgur.com/a/FOaWKN1
Is it supposed to be like this? I thought learning curves are tackling the accuracy scores dependent on the portion of training data independent from any batches/ epochs? Can I actually use this build-in function for batch methods? If yes, which batch size should I choose (full batch or batch size= number of training examples or something in between) and what diagnosis do I get from this? Or how do you usually diagnose bias/ variance problems of a neural network classifier?
Help would be really appreciated!
scikit-learn neural-network mlp
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
Maybe also for this example: What would be my diagnosis here? For me it looks like high bias, since training and crossvalidation score are low. However, looking at batchsize of 200, it looks like as if I would have taken batchsize= number of training examples, training score would be high and it would look like overfitting.
– S.Maria
Mar 26 at 8:17
add a comment |
I am currently working on a classification problem with two classes in ScikitLearn with the solver adam and activation relu. To explore if my classifier suffers from high bias or high variance, I plotted the learning curve with Scikitlearns build-in function:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
I am using a Group-K_Fold crossvalidation with 8 splits.
However, I found that my learning curve is strongly dependent on the batch size of my classifier:
https://imgur.com/a/FOaWKN1
Is it supposed to be like this? I thought learning curves are tackling the accuracy scores dependent on the portion of training data independent from any batches/ epochs? Can I actually use this build-in function for batch methods? If yes, which batch size should I choose (full batch or batch size= number of training examples or something in between) and what diagnosis do I get from this? Or how do you usually diagnose bias/ variance problems of a neural network classifier?
Help would be really appreciated!
scikit-learn neural-network mlp
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
I am currently working on a classification problem with two classes in ScikitLearn with the solver adam and activation relu. To explore if my classifier suffers from high bias or high variance, I plotted the learning curve with Scikitlearns build-in function:
https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
I am using a Group-K_Fold crossvalidation with 8 splits.
However, I found that my learning curve is strongly dependent on the batch size of my classifier:
https://imgur.com/a/FOaWKN1
Is it supposed to be like this? I thought learning curves are tackling the accuracy scores dependent on the portion of training data independent from any batches/ epochs? Can I actually use this build-in function for batch methods? If yes, which batch size should I choose (full batch or batch size= number of training examples or something in between) and what diagnosis do I get from this? Or how do you usually diagnose bias/ variance problems of a neural network classifier?
Help would be really appreciated!
scikit-learn neural-network mlp
scikit-learn neural-network mlp
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
asked Mar 26 at 8:10
S.MariaS.Maria
445 bronze badges
445 bronze badges
Maybe also for this example: What would be my diagnosis here? For me it looks like high bias, since training and crossvalidation score are low. However, looking at batchsize of 200, it looks like as if I would have taken batchsize= number of training examples, training score would be high and it would look like overfitting.
– S.Maria
Mar 26 at 8:17
add a comment |
Maybe also for this example: What would be my diagnosis here? For me it looks like high bias, since training and crossvalidation score are low. However, looking at batchsize of 200, it looks like as if I would have taken batchsize= number of training examples, training score would be high and it would look like overfitting.
– S.Maria
Mar 26 at 8:17
Maybe also for this example: What would be my diagnosis here? For me it looks like high bias, since training and crossvalidation score are low. However, looking at batchsize of 200, it looks like as if I would have taken batchsize= number of training examples, training score would be high and it would look like overfitting.
– S.Maria
Mar 26 at 8:17
Maybe also for this example: What would be my diagnosis here? For me it looks like high bias, since training and crossvalidation score are low. However, looking at batchsize of 200, it looks like as if I would have taken batchsize= number of training examples, training score would be high and it would look like overfitting.
– S.Maria
Mar 26 at 8:17
add a comment |
1 Answer
1
active
oldest
votes
Yes, the learning curve depends on the batch size.
The optimal batch size depends on the type of data and the total volume of the data.
In ideal case batch size of 1 will be best, but in practice, with big volumes of data, this approach is not feasible.
I think you have to do that through experimentation because you can’t easily calculate the optimal value.
Moreover, when you change the batch size you might want to change the learning rate as well so you want to keep the control over the process.
But indeed having a tool to find the optimal (memory and time-wise) batch size is quite interesting.
What is Stochastic Gradient Descent?
Stochastic gradient descent, often abbreviated SGD, is a variation of the gradient descent algorithm that calculates the error and updates the model for each example in the training dataset.
The update of the model for each training example means that stochastic gradient descent is often called an online machine learning algorithm.
What is Batch Gradient Descent?
Batch gradient descent is a variation of the gradient descent algorithm that calculates the error for each example in the training dataset, but only updates the model after all training examples have been evaluated.
One cycle through the entire training dataset is called a training epoch. Therefore, it is often said that batch gradient descent performs model updates at the end of each training epoch.
What is Mini-Batch Gradient Descent?
Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients.
Implementations may choose to sum the gradient over the mini-batch or take the average of the gradient which further reduces the variance of the gradient.
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. It is the most common implementation of gradient descent used in the field of deep learning.
Source: https://machinelearningmastery.com/gentle-introduction-mini-batch-gradient-descent-configure-batch-size/
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
Another question for my understanding of minibatch: Is it correct that weights are updated after processing one batch? However, why is the learning curve changing then after every single training example? There seems to be a misconception from my side, hope anybody can clarify this
– S.Maria
Mar 26 at 11:48
Think I just got it: After each batch the model is updated. However, learning curve evaluates the model for each training examples. The more training example the higher training score, within the confines of the last update of the last batch. However, then the learning curve is useless for full batch, since there will never be an update in my learning curve? Is this correct?
– S.Maria
Mar 26 at 11:58
And is a learning curve useful at all for neural networks, since you are training over several epochs anyway? How do you assess the NN model? Especially in ScikitLearn?
– S.Maria
Mar 26 at 12:29
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
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: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
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
);
);
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%2fstackoverflow.com%2fquestions%2f55352447%2fskikitlearn-learning-curve-strongly-dependent-on-batch-size-of-mlpclassifier%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Yes, the learning curve depends on the batch size.
The optimal batch size depends on the type of data and the total volume of the data.
In ideal case batch size of 1 will be best, but in practice, with big volumes of data, this approach is not feasible.
I think you have to do that through experimentation because you can’t easily calculate the optimal value.
Moreover, when you change the batch size you might want to change the learning rate as well so you want to keep the control over the process.
But indeed having a tool to find the optimal (memory and time-wise) batch size is quite interesting.
What is Stochastic Gradient Descent?
Stochastic gradient descent, often abbreviated SGD, is a variation of the gradient descent algorithm that calculates the error and updates the model for each example in the training dataset.
The update of the model for each training example means that stochastic gradient descent is often called an online machine learning algorithm.
What is Batch Gradient Descent?
Batch gradient descent is a variation of the gradient descent algorithm that calculates the error for each example in the training dataset, but only updates the model after all training examples have been evaluated.
One cycle through the entire training dataset is called a training epoch. Therefore, it is often said that batch gradient descent performs model updates at the end of each training epoch.
What is Mini-Batch Gradient Descent?
Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients.
Implementations may choose to sum the gradient over the mini-batch or take the average of the gradient which further reduces the variance of the gradient.
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. It is the most common implementation of gradient descent used in the field of deep learning.
Source: https://machinelearningmastery.com/gentle-introduction-mini-batch-gradient-descent-configure-batch-size/
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
Another question for my understanding of minibatch: Is it correct that weights are updated after processing one batch? However, why is the learning curve changing then after every single training example? There seems to be a misconception from my side, hope anybody can clarify this
– S.Maria
Mar 26 at 11:48
Think I just got it: After each batch the model is updated. However, learning curve evaluates the model for each training examples. The more training example the higher training score, within the confines of the last update of the last batch. However, then the learning curve is useless for full batch, since there will never be an update in my learning curve? Is this correct?
– S.Maria
Mar 26 at 11:58
And is a learning curve useful at all for neural networks, since you are training over several epochs anyway? How do you assess the NN model? Especially in ScikitLearn?
– S.Maria
Mar 26 at 12:29
add a comment |
Yes, the learning curve depends on the batch size.
The optimal batch size depends on the type of data and the total volume of the data.
In ideal case batch size of 1 will be best, but in practice, with big volumes of data, this approach is not feasible.
I think you have to do that through experimentation because you can’t easily calculate the optimal value.
Moreover, when you change the batch size you might want to change the learning rate as well so you want to keep the control over the process.
But indeed having a tool to find the optimal (memory and time-wise) batch size is quite interesting.
What is Stochastic Gradient Descent?
Stochastic gradient descent, often abbreviated SGD, is a variation of the gradient descent algorithm that calculates the error and updates the model for each example in the training dataset.
The update of the model for each training example means that stochastic gradient descent is often called an online machine learning algorithm.
What is Batch Gradient Descent?
Batch gradient descent is a variation of the gradient descent algorithm that calculates the error for each example in the training dataset, but only updates the model after all training examples have been evaluated.
One cycle through the entire training dataset is called a training epoch. Therefore, it is often said that batch gradient descent performs model updates at the end of each training epoch.
What is Mini-Batch Gradient Descent?
Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients.
Implementations may choose to sum the gradient over the mini-batch or take the average of the gradient which further reduces the variance of the gradient.
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. It is the most common implementation of gradient descent used in the field of deep learning.
Source: https://machinelearningmastery.com/gentle-introduction-mini-batch-gradient-descent-configure-batch-size/
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
Another question for my understanding of minibatch: Is it correct that weights are updated after processing one batch? However, why is the learning curve changing then after every single training example? There seems to be a misconception from my side, hope anybody can clarify this
– S.Maria
Mar 26 at 11:48
Think I just got it: After each batch the model is updated. However, learning curve evaluates the model for each training examples. The more training example the higher training score, within the confines of the last update of the last batch. However, then the learning curve is useless for full batch, since there will never be an update in my learning curve? Is this correct?
– S.Maria
Mar 26 at 11:58
And is a learning curve useful at all for neural networks, since you are training over several epochs anyway? How do you assess the NN model? Especially in ScikitLearn?
– S.Maria
Mar 26 at 12:29
add a comment |
Yes, the learning curve depends on the batch size.
The optimal batch size depends on the type of data and the total volume of the data.
In ideal case batch size of 1 will be best, but in practice, with big volumes of data, this approach is not feasible.
I think you have to do that through experimentation because you can’t easily calculate the optimal value.
Moreover, when you change the batch size you might want to change the learning rate as well so you want to keep the control over the process.
But indeed having a tool to find the optimal (memory and time-wise) batch size is quite interesting.
What is Stochastic Gradient Descent?
Stochastic gradient descent, often abbreviated SGD, is a variation of the gradient descent algorithm that calculates the error and updates the model for each example in the training dataset.
The update of the model for each training example means that stochastic gradient descent is often called an online machine learning algorithm.
What is Batch Gradient Descent?
Batch gradient descent is a variation of the gradient descent algorithm that calculates the error for each example in the training dataset, but only updates the model after all training examples have been evaluated.
One cycle through the entire training dataset is called a training epoch. Therefore, it is often said that batch gradient descent performs model updates at the end of each training epoch.
What is Mini-Batch Gradient Descent?
Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients.
Implementations may choose to sum the gradient over the mini-batch or take the average of the gradient which further reduces the variance of the gradient.
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. It is the most common implementation of gradient descent used in the field of deep learning.
Source: https://machinelearningmastery.com/gentle-introduction-mini-batch-gradient-descent-configure-batch-size/
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
Yes, the learning curve depends on the batch size.
The optimal batch size depends on the type of data and the total volume of the data.
In ideal case batch size of 1 will be best, but in practice, with big volumes of data, this approach is not feasible.
I think you have to do that through experimentation because you can’t easily calculate the optimal value.
Moreover, when you change the batch size you might want to change the learning rate as well so you want to keep the control over the process.
But indeed having a tool to find the optimal (memory and time-wise) batch size is quite interesting.
What is Stochastic Gradient Descent?
Stochastic gradient descent, often abbreviated SGD, is a variation of the gradient descent algorithm that calculates the error and updates the model for each example in the training dataset.
The update of the model for each training example means that stochastic gradient descent is often called an online machine learning algorithm.
What is Batch Gradient Descent?
Batch gradient descent is a variation of the gradient descent algorithm that calculates the error for each example in the training dataset, but only updates the model after all training examples have been evaluated.
One cycle through the entire training dataset is called a training epoch. Therefore, it is often said that batch gradient descent performs model updates at the end of each training epoch.
What is Mini-Batch Gradient Descent?
Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients.
Implementations may choose to sum the gradient over the mini-batch or take the average of the gradient which further reduces the variance of the gradient.
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. It is the most common implementation of gradient descent used in the field of deep learning.
Source: https://machinelearningmastery.com/gentle-introduction-mini-batch-gradient-descent-configure-batch-size/
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
edited Mar 26 at 11:59
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
answered Mar 26 at 10:40
Antoan MilkovAntoan Milkov
1,73211 silver badges23 bronze badges
1,73211 silver badges23 bronze badges
Another question for my understanding of minibatch: Is it correct that weights are updated after processing one batch? However, why is the learning curve changing then after every single training example? There seems to be a misconception from my side, hope anybody can clarify this
– S.Maria
Mar 26 at 11:48
Think I just got it: After each batch the model is updated. However, learning curve evaluates the model for each training examples. The more training example the higher training score, within the confines of the last update of the last batch. However, then the learning curve is useless for full batch, since there will never be an update in my learning curve? Is this correct?
– S.Maria
Mar 26 at 11:58
And is a learning curve useful at all for neural networks, since you are training over several epochs anyway? How do you assess the NN model? Especially in ScikitLearn?
– S.Maria
Mar 26 at 12:29
add a comment |
Another question for my understanding of minibatch: Is it correct that weights are updated after processing one batch? However, why is the learning curve changing then after every single training example? There seems to be a misconception from my side, hope anybody can clarify this
– S.Maria
Mar 26 at 11:48
Think I just got it: After each batch the model is updated. However, learning curve evaluates the model for each training examples. The more training example the higher training score, within the confines of the last update of the last batch. However, then the learning curve is useless for full batch, since there will never be an update in my learning curve? Is this correct?
– S.Maria
Mar 26 at 11:58
And is a learning curve useful at all for neural networks, since you are training over several epochs anyway? How do you assess the NN model? Especially in ScikitLearn?
– S.Maria
Mar 26 at 12:29
Another question for my understanding of minibatch: Is it correct that weights are updated after processing one batch? However, why is the learning curve changing then after every single training example? There seems to be a misconception from my side, hope anybody can clarify this
– S.Maria
Mar 26 at 11:48
Another question for my understanding of minibatch: Is it correct that weights are updated after processing one batch? However, why is the learning curve changing then after every single training example? There seems to be a misconception from my side, hope anybody can clarify this
– S.Maria
Mar 26 at 11:48
Think I just got it: After each batch the model is updated. However, learning curve evaluates the model for each training examples. The more training example the higher training score, within the confines of the last update of the last batch. However, then the learning curve is useless for full batch, since there will never be an update in my learning curve? Is this correct?
– S.Maria
Mar 26 at 11:58
Think I just got it: After each batch the model is updated. However, learning curve evaluates the model for each training examples. The more training example the higher training score, within the confines of the last update of the last batch. However, then the learning curve is useless for full batch, since there will never be an update in my learning curve? Is this correct?
– S.Maria
Mar 26 at 11:58
And is a learning curve useful at all for neural networks, since you are training over several epochs anyway? How do you assess the NN model? Especially in ScikitLearn?
– S.Maria
Mar 26 at 12:29
And is a learning curve useful at all for neural networks, since you are training over several epochs anyway? How do you assess the NN model? Especially in ScikitLearn?
– S.Maria
Mar 26 at 12:29
add a comment |
Got a question that you can’t ask on public Stack Overflow? Learn more about sharing private information with Stack Overflow for Teams.
Got a question that you can’t ask on public Stack Overflow? Learn more about sharing private information with Stack Overflow for Teams.
Thanks for contributing an answer to Stack Overflow!
- 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.
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%2fstackoverflow.com%2fquestions%2f55352447%2fskikitlearn-learning-curve-strongly-dependent-on-batch-size-of-mlpclassifier%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
Maybe also for this example: What would be my diagnosis here? For me it looks like high bias, since training and crossvalidation score are low. However, looking at batchsize of 200, it looks like as if I would have taken batchsize= number of training examples, training score would be high and it would look like overfitting.
– S.Maria
Mar 26 at 8:17