Just why is Bayes so naive?

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In this blog post, we’ll look at how to apply some of the theories for machine learning that are acquainted with Bayes’ theorem and foundational principles of Bayesian statistics. Classification problems are a natural implementation of Bayes’ theorem when you’re trying to predict a…


A quick debriefing on a really cool supervised learning algorithm

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K-Nearest Neighbors, or KNN, is a supervised learning algorithm that can be applied on classification and regression problems. KNN is a distance-based classifier, which means it automatically implies that the closer two points are, the more identical they are. Euclidean…


Discussing the two most useful metrics that are used to describe a model’s efficiency

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In my most recent blog post, I went over two of the easier and more common metrics used to explore model performance in machine learning, precision and recall. …


Precision and recall are two of the most fundamental evaluation metrics that we have at our hands.

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It’s imperative to compare your models to each other and pick the best fit models when performing tasks about classification. When you are estimating values in regression, it makes sense to speak about…


How to automate the process of selecting features

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In data science, there are many different approaches to building features to model complicated relationships — although, this may sometimes be troublesome. …


Hooray for calculus!

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You have probably heard about the central principle of mathematical functions if you have studied linear regression. You can articulate this with the following example — assume that you have used the number of bathrooms in a house as a predictor and the house rental price as…


A quick discussion on computational complexity

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In this blog post, I will be exposing you to some of the complexity in computation in relation to OLS regression. You will read about this concept and see that this might not be the most powerful algorithm for estimating regression parameters while regression…


Improve your data to boost your regression results

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Normal features, or features that are as normally distributed as possible, will lead to better outcomes. This is what makes scaling and the normalization of features in regression modeling so significant. There are a number of ways to scale your features, and…


The biggest issue hindering quality results in regression modeling

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If you are familiar with data science, especially with regression modelling, then you are probably familiar with the concepts of covariance and correlation. …


How to test the required assumptions

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Regression diagnostics are a series of regression analysis techniques that test the validity of a model in a variety of ways. These techniques can include an examination of the underlying mathematical assumptions of the model, an overview of the model structure through the consideration…

Acusio Bivona

Fitness, Sports, Data — And not necessarily in that order

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