Finding a logistic regression model which can achieve zero error on a training set training data for a binary...
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Finding a logistic regression model which can achieve zero error on a training set training data for a binary classification problem with two features
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Finding a logistic regression model which can achieve zero error on a training set training data for a binary classification problem with two features
What is the best training method for 15-30k records with 5-12 features to classify data into 2 groups?Training set as donor for test set in binary classification problembuilding a classification model for strictly binary dataHow to best to use Continuous value features with discreet values for logistic regression based binary classification problemWhy is the reconstruction error for my training set larger than my test error using PCA on the MNIST data set?Stacking models which trained by different features in a data set for a classification problemExpectation and kth factorial momentFinding value up to a constant of proportionality and Correlation Matrix questionFinding pdf with more than one random variableLikelihood of Gamma Distribution
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Not sure where to begin with this question, can anyone help out?
machine-learning self-study mathematical-statistics
edited 1 hour ago
Bryan Krause
697212
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add a comment |
$begingroup$
Not sure where to begin with this question, can anyone help out?
machine-learning self-study mathematical-statistics
edited 1 hour ago
Bryan Krause
697212
$endgroup$
add a comment |
$begingroup$
Not sure where to begin with this question, can anyone help out?
machine-learning self-study mathematical-statistics
edited 1 hour ago
Bryan Krause
697212
$endgroup$
Not sure where to begin with this question, can anyone help out?
machine-learning self-study mathematical-statistics
machine-learning self-study mathematical-statistics
edited 1 hour ago
Bryan Krause
697212
edited 1 hour ago
Bryan Krause
697212
edited 1 hour ago
Bryan Krause
697212
edited 1 hour ago
Bryan Krause
697212
edited 1 hour ago
Bryan Krause
697212
697212
asked 1 hour ago
user239276
add a comment |
add a comment |
1 Answer
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Logistic regression is a linear classifier, i.e. it draws a line (2D datasets) and classifies accordingly (one side is class 0, other side is class 1). So, if classes can be distinguished by a line (or hyperplane in higher dimensions), it is said that the dataset is linearly separable, though this dataset is not. One way to tackle this issue is creating new features, or applying transformations. For example, this dataset seems to be separable if you think radially, i.e. $R>alpha$, where $R$ is the radius, or distance to origin, which can be found by $R=sqrt{X_1^2+X_2^2}$. Constructing a logistic regression using this feature only, results in perfect classification.
answered 1 hour ago
gunesgunes
5,2901113
$endgroup$
$begingroup$
By log-reg, do you mean a logistic regression model? Thanks for your help by the way!
$endgroup$
– user239276
1 hour ago
$begingroup$
yes, sorry for ambiguity.
$endgroup$
– gunes
1 hour ago
$begingroup$
@gunes This might be a bit too much of an answer for a self-study question, although I don't typically police those here and am not certain where exactly the community falls on these sorts of questions besides what is included in the tag info.
$endgroup$
– Bryan Krause
1 hour ago
1
$begingroup$
(+1) It's worth noting that this is essentially using a very simple Radial Basis Network with logistic loss
$endgroup$
– Cliff AB
54 mins ago
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
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active
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votes
$begingroup$
Logistic regression is a linear classifier, i.e. it draws a line (2D datasets) and classifies accordingly (one side is class 0, other side is class 1). So, if classes can be distinguished by a line (or hyperplane in higher dimensions), it is said that the dataset is linearly separable, though this dataset is not. One way to tackle this issue is creating new features, or applying transformations. For example, this dataset seems to be separable if you think radially, i.e. $R>alpha$, where $R$ is the radius, or distance to origin, which can be found by $R=sqrt{X_1^2+X_2^2}$. Constructing a logistic regression using this feature only, results in perfect classification.
answered 1 hour ago
gunesgunes
5,2901113
$endgroup$
$begingroup$
By log-reg, do you mean a logistic regression model? Thanks for your help by the way!
$endgroup$
– user239276
1 hour ago
$begingroup$
yes, sorry for ambiguity.
$endgroup$
– gunes
1 hour ago
$begingroup$
@gunes This might be a bit too much of an answer for a self-study question, although I don't typically police those here and am not certain where exactly the community falls on these sorts of questions besides what is included in the tag info.
$endgroup$
– Bryan Krause
1 hour ago
1
$begingroup$
(+1) It's worth noting that this is essentially using a very simple Radial Basis Network with logistic loss
$endgroup$
– Cliff AB
54 mins ago
add a comment |
$begingroup$
Logistic regression is a linear classifier, i.e. it draws a line (2D datasets) and classifies accordingly (one side is class 0, other side is class 1). So, if classes can be distinguished by a line (or hyperplane in higher dimensions), it is said that the dataset is linearly separable, though this dataset is not. One way to tackle this issue is creating new features, or applying transformations. For example, this dataset seems to be separable if you think radially, i.e. $R>alpha$, where $R$ is the radius, or distance to origin, which can be found by $R=sqrt{X_1^2+X_2^2}$. Constructing a logistic regression using this feature only, results in perfect classification.
answered 1 hour ago
gunesgunes
5,2901113
$endgroup$
$begingroup$
By log-reg, do you mean a logistic regression model? Thanks for your help by the way!
$endgroup$
– user239276
1 hour ago
$begingroup$
yes, sorry for ambiguity.
$endgroup$
– gunes
1 hour ago
$begingroup$
@gunes This might be a bit too much of an answer for a self-study question, although I don't typically police those here and am not certain where exactly the community falls on these sorts of questions besides what is included in the tag info.
$endgroup$
– Bryan Krause
1 hour ago
1
$begingroup$
(+1) It's worth noting that this is essentially using a very simple Radial Basis Network with logistic loss
$endgroup$
– Cliff AB
54 mins ago
add a comment |
$begingroup$
Logistic regression is a linear classifier, i.e. it draws a line (2D datasets) and classifies accordingly (one side is class 0, other side is class 1). So, if classes can be distinguished by a line (or hyperplane in higher dimensions), it is said that the dataset is linearly separable, though this dataset is not. One way to tackle this issue is creating new features, or applying transformations. For example, this dataset seems to be separable if you think radially, i.e. $R>alpha$, where $R$ is the radius, or distance to origin, which can be found by $R=sqrt{X_1^2+X_2^2}$. Constructing a logistic regression using this feature only, results in perfect classification.
answered 1 hour ago
gunesgunes
5,2901113
$endgroup$
Logistic regression is a linear classifier, i.e. it draws a line (2D datasets) and classifies accordingly (one side is class 0, other side is class 1). So, if classes can be distinguished by a line (or hyperplane in higher dimensions), it is said that the dataset is linearly separable, though this dataset is not. One way to tackle this issue is creating new features, or applying transformations. For example, this dataset seems to be separable if you think radially, i.e. $R>alpha$, where $R$ is the radius, or distance to origin, which can be found by $R=sqrt{X_1^2+X_2^2}$. Constructing a logistic regression using this feature only, results in perfect classification.
answered 1 hour ago
gunesgunes
5,2901113
edited 1 hour ago
answered 1 hour ago
gunesgunes
5,2901113
answered 1 hour ago
gunesgunes
5,2901113
answered 1 hour ago
gunesgunes
5,2901113
5,2901113
$begingroup$
By log-reg, do you mean a logistic regression model? Thanks for your help by the way!
$endgroup$
– user239276
1 hour ago
$begingroup$
yes, sorry for ambiguity.
$endgroup$
– gunes
1 hour ago
$begingroup$
@gunes This might be a bit too much of an answer for a self-study question, although I don't typically police those here and am not certain where exactly the community falls on these sorts of questions besides what is included in the tag info.
$endgroup$
– Bryan Krause
1 hour ago
1
$begingroup$
(+1) It's worth noting that this is essentially using a very simple Radial Basis Network with logistic loss
$endgroup$
– Cliff AB
54 mins ago
add a comment |
$begingroup$
By log-reg, do you mean a logistic regression model? Thanks for your help by the way!
$endgroup$
– user239276
1 hour ago
$begingroup$
yes, sorry for ambiguity.
$endgroup$
– gunes
1 hour ago
$begingroup$
@gunes This might be a bit too much of an answer for a self-study question, although I don't typically police those here and am not certain where exactly the community falls on these sorts of questions besides what is included in the tag info.
$endgroup$
– Bryan Krause
1 hour ago
1
$begingroup$
(+1) It's worth noting that this is essentially using a very simple Radial Basis Network with logistic loss
$endgroup$
– Cliff AB
54 mins ago
$begingroup$
By log-reg, do you mean a logistic regression model? Thanks for your help by the way!
$endgroup$
– user239276
1 hour ago
$begingroup$
By log-reg, do you mean a logistic regression model? Thanks for your help by the way!
$endgroup$
– user239276
1 hour ago
$begingroup$
yes, sorry for ambiguity.
$endgroup$
– gunes
1 hour ago
$begingroup$
yes, sorry for ambiguity.
$endgroup$
– gunes
1 hour ago
$begingroup$
@gunes This might be a bit too much of an answer for a self-study question, although I don't typically police those here and am not certain where exactly the community falls on these sorts of questions besides what is included in the tag info.
$endgroup$
– Bryan Krause
1 hour ago
$begingroup$
@gunes This might be a bit too much of an answer for a self-study question, although I don't typically police those here and am not certain where exactly the community falls on these sorts of questions besides what is included in the tag info.
$endgroup$
– Bryan Krause
1 hour ago
1
1
$begingroup$
(+1) It's worth noting that this is essentially using a very simple Radial Basis Network with logistic loss
$endgroup$
– Cliff AB
54 mins ago
$begingroup$
(+1) It's worth noting that this is essentially using a very simple Radial Basis Network with logistic loss
$endgroup$
– Cliff AB
54 mins ago
add a comment |
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