When does fitting happen in KNN?2019 Community Moderator ElectionHow to continue incremental learning when a categorical variable has been assigned additional category labels?When to use Multinomial Naive Bayes?Classification using Orange 3When is feature transformation required?How would I apply anomaly detection to time series data in LSTM?Why does a machine learning algorithm need a bias?Décision tree, How to see under/over fitting with just looking at the leafs?How can someone avoid over fitting or data leak in ridge and lasso regression when the training score is high and test score is low?Purpose of test data in binary classificationModel accuracy changes when target attribute change
Convert two switches to a dual stack, and add outlet - possible here?
Client team has low performances and low technical skills: we always fix their work and now they stop collaborate with us. How to solve?
DC-DC converter from low voltage at high current, to high voltage at low current
Can a vampire attack twice with their claws using multiattack?
What's the point of deactivating Num Lock on login screens?
If human space travel is limited by the G force vulnerability, is there a way to counter G forces?
Uncaught TypeError: 'set' on proxy: trap returned falsish for property Name
Mutually beneficial digestive system symbiotes
Today is the Center
Does detail obscure or enhance action?
Why doesn't Newton's third law mean a person bounces back to where they started when they hit the ground?
Mortgage Pre-approval / Loan - Apply Alone or with Fiancée?
Why do I get two different answers for this counting problem?
Operational amplifier as a comparator at high frequency
How much of data wrangling is a data scientist's job?
I'm flying to France today and my passport expires in less than 2 months
Is it legal for company to use my work email to pretend I still work there?
Is it inappropriate for a student to attend their mentor's dissertation defense?
Accidentally leaked the solution to an assignment, what to do now? (I'm the prof)
Why is consensus so controversial in Britain?
How is the claim "I am in New York only if I am in America" the same as "If I am in New York, then I am in America?
Do I have a twin with permutated remainders?
meaning of に in 本当に?
Could an aircraft fly or hover using only jets of compressed air?
When does fitting happen in KNN?
2019 Community Moderator ElectionHow to continue incremental learning when a categorical variable has been assigned additional category labels?When to use Multinomial Naive Bayes?Classification using Orange 3When is feature transformation required?How would I apply anomaly detection to time series data in LSTM?Why does a machine learning algorithm need a bias?Décision tree, How to see under/over fitting with just looking at the leafs?How can someone avoid over fitting or data leak in ridge and lasso regression when the training score is high and test score is low?Purpose of test data in binary classificationModel accuracy changes when target attribute change
$begingroup$
In training session, model fitting happens to reduce error. But does KNN do this?
Reducing error only happens due to changing K value and number of features, isn't it?
So training set and test set is only for do things below, right?
- Train model with training set
- Given test sample, model finds K-nearest neighbors in training set
- Do classification or regression for test sample
- Find accuracy with MSE or RMSE
machine-learning-model
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
$endgroup$
add a comment |
$begingroup$
In training session, model fitting happens to reduce error. But does KNN do this?
Reducing error only happens due to changing K value and number of features, isn't it?
So training set and test set is only for do things below, right?
- Train model with training set
- Given test sample, model finds K-nearest neighbors in training set
- Do classification or regression for test sample
- Find accuracy with MSE or RMSE
machine-learning-model
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
$endgroup$
add a comment |
$begingroup$
In training session, model fitting happens to reduce error. But does KNN do this?
Reducing error only happens due to changing K value and number of features, isn't it?
So training set and test set is only for do things below, right?
- Train model with training set
- Given test sample, model finds K-nearest neighbors in training set
- Do classification or regression for test sample
- Find accuracy with MSE or RMSE
machine-learning-model
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
$endgroup$
In training session, model fitting happens to reduce error. But does KNN do this?
Reducing error only happens due to changing K value and number of features, isn't it?
So training set and test set is only for do things below, right?
- Train model with training set
- Given test sample, model finds K-nearest neighbors in training set
- Do classification or regression for test sample
- Find accuracy with MSE or RMSE
machine-learning-model
machine-learning-model
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
asked Mar 27 at 20:29
Jinwoo LeeJinwoo Lee
82
82
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Short version: this is just terminology, but arguably K-NN doesn't actually fit a model.
Conceptually, K-NN is a lazy learning model. This means that, in a sense, there's no fitting until a new instance arrives to be classified or a value has to be predicted (depending if you're using K-NN for classifying or regression, both are possible). Even then, using the term "fitting the model" is a bit out-of-place, in my opinion.
I think your confusion is in the sentence "model fitting happens to reduce error". No, it does not. Model fitting is simply getting a model (for example, a family of data distributions) and fitting the model by finding the parameters that better describe the data (thus choosing a member from that family).
Maybe you're thinking of neural network's epochs as "fitting"? What happens there is that there's a family of possible models (all the possible values for each weight in the network), and fitting the model is just finding the best possible values. The fact that neural networks do so in an iterative manner does not mean that fitting is an iterative process, fitting is the end result.
So training set and test set is only for do things below, right?
Well, that's true for every model, yes. Training and test set separation serves the only purpose of evaluating the model on the chosen metrics. At the end of the day, the final model you choose to deploy, be it lazy or eager, will use all the available data (or a portion/transformation of it, in some cases), not just the training set.
edited Mar 27 at 21:39
Esmailian
2,621318
answered Mar 27 at 21:32
MephyMephy
728519
$endgroup$
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
return StackExchange.using("mathjaxEditing", function ()
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix)
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
);
);
, "mathjax-editing");
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
);
);
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%2f48108%2fwhen-does-fitting-happen-in-knn%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
$begingroup$
Short version: this is just terminology, but arguably K-NN doesn't actually fit a model.
Conceptually, K-NN is a lazy learning model. This means that, in a sense, there's no fitting until a new instance arrives to be classified or a value has to be predicted (depending if you're using K-NN for classifying or regression, both are possible). Even then, using the term "fitting the model" is a bit out-of-place, in my opinion.
I think your confusion is in the sentence "model fitting happens to reduce error". No, it does not. Model fitting is simply getting a model (for example, a family of data distributions) and fitting the model by finding the parameters that better describe the data (thus choosing a member from that family).
Maybe you're thinking of neural network's epochs as "fitting"? What happens there is that there's a family of possible models (all the possible values for each weight in the network), and fitting the model is just finding the best possible values. The fact that neural networks do so in an iterative manner does not mean that fitting is an iterative process, fitting is the end result.
So training set and test set is only for do things below, right?
Well, that's true for every model, yes. Training and test set separation serves the only purpose of evaluating the model on the chosen metrics. At the end of the day, the final model you choose to deploy, be it lazy or eager, will use all the available data (or a portion/transformation of it, in some cases), not just the training set.
edited Mar 27 at 21:39
Esmailian
2,621318
answered Mar 27 at 21:32
MephyMephy
728519
$endgroup$
add a comment |
$begingroup$
Short version: this is just terminology, but arguably K-NN doesn't actually fit a model.
Conceptually, K-NN is a lazy learning model. This means that, in a sense, there's no fitting until a new instance arrives to be classified or a value has to be predicted (depending if you're using K-NN for classifying or regression, both are possible). Even then, using the term "fitting the model" is a bit out-of-place, in my opinion.
I think your confusion is in the sentence "model fitting happens to reduce error". No, it does not. Model fitting is simply getting a model (for example, a family of data distributions) and fitting the model by finding the parameters that better describe the data (thus choosing a member from that family).
Maybe you're thinking of neural network's epochs as "fitting"? What happens there is that there's a family of possible models (all the possible values for each weight in the network), and fitting the model is just finding the best possible values. The fact that neural networks do so in an iterative manner does not mean that fitting is an iterative process, fitting is the end result.
So training set and test set is only for do things below, right?
Well, that's true for every model, yes. Training and test set separation serves the only purpose of evaluating the model on the chosen metrics. At the end of the day, the final model you choose to deploy, be it lazy or eager, will use all the available data (or a portion/transformation of it, in some cases), not just the training set.
edited Mar 27 at 21:39
Esmailian
2,621318
answered Mar 27 at 21:32
MephyMephy
728519
$endgroup$
add a comment |
$begingroup$
Short version: this is just terminology, but arguably K-NN doesn't actually fit a model.
Conceptually, K-NN is a lazy learning model. This means that, in a sense, there's no fitting until a new instance arrives to be classified or a value has to be predicted (depending if you're using K-NN for classifying or regression, both are possible). Even then, using the term "fitting the model" is a bit out-of-place, in my opinion.
I think your confusion is in the sentence "model fitting happens to reduce error". No, it does not. Model fitting is simply getting a model (for example, a family of data distributions) and fitting the model by finding the parameters that better describe the data (thus choosing a member from that family).
Maybe you're thinking of neural network's epochs as "fitting"? What happens there is that there's a family of possible models (all the possible values for each weight in the network), and fitting the model is just finding the best possible values. The fact that neural networks do so in an iterative manner does not mean that fitting is an iterative process, fitting is the end result.
So training set and test set is only for do things below, right?
Well, that's true for every model, yes. Training and test set separation serves the only purpose of evaluating the model on the chosen metrics. At the end of the day, the final model you choose to deploy, be it lazy or eager, will use all the available data (or a portion/transformation of it, in some cases), not just the training set.
edited Mar 27 at 21:39
Esmailian
2,621318
answered Mar 27 at 21:32
MephyMephy
728519
$endgroup$
Short version: this is just terminology, but arguably K-NN doesn't actually fit a model.
Conceptually, K-NN is a lazy learning model. This means that, in a sense, there's no fitting until a new instance arrives to be classified or a value has to be predicted (depending if you're using K-NN for classifying or regression, both are possible). Even then, using the term "fitting the model" is a bit out-of-place, in my opinion.
I think your confusion is in the sentence "model fitting happens to reduce error". No, it does not. Model fitting is simply getting a model (for example, a family of data distributions) and fitting the model by finding the parameters that better describe the data (thus choosing a member from that family).
Maybe you're thinking of neural network's epochs as "fitting"? What happens there is that there's a family of possible models (all the possible values for each weight in the network), and fitting the model is just finding the best possible values. The fact that neural networks do so in an iterative manner does not mean that fitting is an iterative process, fitting is the end result.
So training set and test set is only for do things below, right?
Well, that's true for every model, yes. Training and test set separation serves the only purpose of evaluating the model on the chosen metrics. At the end of the day, the final model you choose to deploy, be it lazy or eager, will use all the available data (or a portion/transformation of it, in some cases), not just the training set.
edited Mar 27 at 21:39
Esmailian
2,621318
answered Mar 27 at 21:32
MephyMephy
728519
edited Mar 27 at 21:39
Esmailian
2,621318
edited Mar 27 at 21:39
Esmailian
2,621318
edited Mar 27 at 21:39
Esmailian
2,621318
2,621318
answered Mar 27 at 21:32
MephyMephy
728519
answered Mar 27 at 21:32
MephyMephy
728519
answered Mar 27 at 21:32
MephyMephy
728519
728519
add a comment |
add a comment |
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%2f48108%2fwhen-does-fitting-happen-in-knn%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
