A simple probabilistic way to Logistic regression….

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When I started to learn classification techniques, I searched many books and many blogs for the probabilistic approach and why we need to take some particular functions. Because though it is simple concept, many of us do not understand the play behind the scene.

In data science, we often deal with classification tasks. For classification tasks, a simple model is LOGISTIC REGRESSION. Everybody use this model one or the other time. If we are using python, sklearn library will provide implementation of this model and it is more or less 3 to 4…

An amazing way to transform given basis into orthonormal basis

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In Linear Algebra, We usually deal with vector spaces and its subspaces. It is easier to perform any operations, if we have orthogonal basis of subspace. Standard basis are orthogonal and orthonormal. Hence it will be easy when we work with standard basis. So what do we do if we have basis of subspace which are not orthogonal? There comes Gram-Schmidt Orthogonalization process. Let us walkthrough the process.

A subspace, here inner product space, has linearly independent set of vectors in the basis. The number of vectors in the basis is dimension of the subspace. In orthogonalization process we take…

Praveen Hegde

MSc Statistics student, Machine Learning Learner

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