Questions tagged [linearly-separable]

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Can reducing information improve regression prediction?

Variable A is either 0 or 1. It is 0 if the sum of variables a + b + c + d … is less than some constant threshold, and is 1 if the sum of variables a + b + c + d … is greater than some constant ...
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Why does Logistic Regression perform better than machine learning models in clinical prediction studies

I am developing binary classification models to predict a medical condition in my dataset. My results show that both Logistic Regression and Linear SVM consistently outperformed other ML algorithms (...
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Intuition behind replacing constraint in equation for Optimal Separating Hyperplane

I am reading "Optimal Separating Hyperplane" section of the book - Elements of Statistical Learning which is described on page 132 as follows: My questions: The constraint $||\beta|| = 1$ ...
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Visualizing the equation for separating hyperplane

I was wondering if I can visualize with the example the fact that for all points $x$ on the separating hyperplane, the following equation holds true: w^T.x+w_0=0\quad\quad\quad \text{... equation (1)...
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Does linear classifier creates linear decision boundary in the input feature space?

I read a lot , but still not able to get the following concepts -: (1) If a classifier is given, how do we know whether its a linear or non linear classifier? (Interested in step by step procedure to ...
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Can this dataset be separated linearly?

Is this dataset linearly separable? If not, can it be converted into one by applying some function as it seems to follow the same pattern? Also, which classification algorithms could be used to fit ...
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231 views

Decision boundary in a classification task

I have 1000 data points from the bivariate normal distribution $\mathcal{N}$ with mean $(0,0)$ and variance $\sigma_1^2=\sigma_2^2=10$ with the covariances being $0$. Also there are 20 more points ...
• 141
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questions about logistic regression

In the following Linear Regression discussion I didn't understand a few things: So my questions are: In the third slide: What does this probability means $P\left(y_i|x_i\right)$ and accordingly what ...
• 157
1 vote
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148 views

PCA vs.KernelPCA: which one to use for high dimensional data?

I have a dataset which contains a lot of features (>>3). For computational reasons, I would like to apply a dimensionality reduction. At this point I could use different techniques: standard PCA ...
3 votes
2 answers
96 views

Kernel selections in SVM

I want to understand the kernel selection rationale in SVM. Some basic things that I understand is if data is linear, then we must go for linear kernel and if it is non-linear, then others. But the ...
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4 votes
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Is a data set considered to be linearly separable if it can only be separated by multiple hyperplanes?

For example, on the linear separability Wikipedia article, the following example is given: They say "The following example would need two straight lines and thus is not linearly separable". On the ...
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