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Lasso vs Ridge vs Elastic Net – Machine learning


Lasso, Ridge, and Elastic Net are excellent methods to improve the performance of your linear model. This post will summarise the usage of these regularization techniques. Bias: Biases are the underlying assumptions that are made by data to simplify the target function. Bias does help us generalize the data better and make the model less sensitive to single data points. It also decreases the...

Sparse Matrices for Machine Learning quick note

In machine learning, many matrices are sparse. It is essential to know how to handle this kind of matrix. Sparse vs Dense Matrix First, it is good to know that sparse matrix looks similar to a normal matrix, with rows, columns or other indexes. But a sparse matrix is comprised of mostly zero (0s) values. They are distinct from dense matrices with mostly non-zero values. A matrix is sparse if many...

Machine learning quick note

Machine learning is a terminology to describe the uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. You can think of machine learning as the brains behind AI technologies, and AI technologies do the actions. More technically, machine learning...

K-Means vs K-Nearest neighbours quick note


These are completely different methods in machine learning. The fact that they both have the letter K in their name is a coincidence. K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification. The typical k-means...

Dealing with missing data


In real-world data, there are some instances where a particular element is absent because of various reasons, such as corrupt data, failure to load the information, or incomplete extraction. Handling the missing values is one of the greatest challenges faced by analysts because making the right decision on how to handle it generates robust data models. Let us look at different ways of imputing...

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