In my previous article about Intuitively, how can we understand different classification algorithms, I introduced the main principles of classification algorithms. However, the toy data I used was quite simple, almost linearly separable data; in real life, the data is almost always non-linear, so we should make our algorithm able to tackle non linearly separable data.
Simple Logistic Regression and non-linear data
Let’s compare how logistic regression behaves with almost linearly separable data and non-linearly separable data.
With the two toy data below, we can see that Logistic Regression helps us find the decision boundary when the data is almost linearly separable, but when the data is not linearly separable data, Logistic Regression is not capable to find a clear decision boundary. It is understandable because Logistic Regression is only able to separate the data into two parts.

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