Machine Learning Models: Linear RegressionIsaiah NieldsBlockedUnblockFollowFollowingFeb 1Over the last few weeks, I have been quickly studying my way through Deep…

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## Logistic Regression: The good parts

Logistic Regression: The good partsEverything you need to know about it. Thalles SilvaBlockedUnblockFollowFollowingFeb 5In the last post, we tackled the problem of…

Continue Reading## Dealing with Imbalanced Data

Let’s try our logistic regression again with the balanced training data. Our recall score increased, but F1 is much lower…

Continue Reading## Understanding Studies of Racial Demarcations

Understanding Studies of Racial DemarcationsOghenovo Obrimah, PhDBlockedUnblockFollowFollowingFeb 3Studies of racial demarcations typically are implemented in context of what are referred…

Continue Reading## Linear Regression From Scratch With Python

That’s what the error function is for — it calculates the total error of your line. We’ll be using an error function…

Continue Reading## ML Algorithms: One SD (σ)

ML Algorithms: One SD (σ)Sagi ShaierBlockedUnblockFollowFollowingJan 30The obvious questions to ask when facing a wide variety of machine learning algorithms, is…

Continue Reading## Exploring, visualizing, and modeling the Minnesota Vikings offense

Exploring, visualizing, and modeling the Minnesota Vikings offenseWilliam ButlerBlockedUnblockFollowFollowingJan 29After a brief search, I acquired play-by-play data for the entire 2018…

Continue Reading## Hierarchical Bayesian Modeling for Ford GoBike Ridership with PyMC3 — Part II

Probably not in most cases. I want understanding and results. We can achieve this with Bayesian inference models, and PyMC3…

Continue Reading## Fitting a Neural Network Using Randomized Optimization in Python

Fitting a Neural Network Using Randomized Optimization in PythonHow randomized optimization can be used to find the optimal weights for machine…

Continue Reading## Supervised Learning: Basics of Linear Regression

Supervised Learning: Basics of Linear RegressionVictor RomanBlockedUnblockFollowFollowingJan 151. IntroductionRegression analysis is a subfield of supervised machine learning. It aims to…

Continue Reading## Introduction to Linear Regression and Polynomial Regression

Introduction to Linear Regression and Polynomial RegressionAyush PantBlockedUnblockFollowFollowingJan 13IntroductionIn this blog, we will discuss two important topics that will form…

Continue Reading## How to Perform Lasso and Ridge Regression in Python

How to Perform Lasso and Ridge Regression in PythonA quick tutorial on how to use lasso and ridge regression to improve…

Continue Reading## Interpreting the coefficients of linear regression

Source: UnsplashInterpreting the coefficients of linear regressionEryk LewinsonBlockedUnblockFollowFollowingJan 13Nowadays there is a plethora of machine learning algorithms we can try…

Continue Reading## Regression Analysis: Generalised Linear Model

Regression Analysis: Generalised Linear ModelPart II of IIISung KimBlockedUnblockFollowFollowingJan 10This article requires basic knowledge of Linear Regression and is a pre-requisite for…

Continue Reading## A Complete View of Decision Trees and SVM in Machine Learning

Let’s assume our data has p inputs and a response for each of N observations. To construct a regression tree:Consider…

Continue Reading## Regression Analysis: Linear Regression

Regression Analysis: Linear RegressionPart I of IIISung KimBlockedUnblockFollowFollowingDec 16, 2018http://dataaspirant. com/2014/10/02/linear-regression/1. IntroductionRegression analysis is perhaps the most fundamental statistical modeling technique…

Continue Reading## A Simple Guide to the Basics of A.I.

One of the main concerns in machine learning is finding a best-fit line or curve that is just curvy enough…

Continue Reading## Attempting to predict deals on Shark Tank

Combined, each prediction will be closer to the true class than individual predictions.RFC = RandomForestClassifier()RFC.fit(X_train, y_train)y_pred = RFC.predict(X_test)Accuracy = accuracy_score(y_test,…

Continue Reading## Intuitions on L1 and L2 Regularisation

Here’s a primer on norms:1-norm (also known as L1 norm)2-norm (also known as L2 norm or Euclidean norm)p-normA linear regression model…

Continue Reading## Simply Explained Logistic Regression with Example in R

Keep in mind that the main premise of logistic regression is still based upon a typical regression model with a…

Continue Reading## A Guide to Machine Learning in R for Beginners : Part 4

We will also study in detail about Linear Regression with code in RFunction: A function is a relationship where each…

Continue Reading## March Madness — Predicting the NCAA Tournament

We were successfully able to score a more accurate prediction each time we added complexities into our model, and we…

Continue Reading## Implementing Multiclass Logistic Regression using BigQuery ML

Other algorithm specific hyper-parameters should be mentioned in this block as well.# Train a modeltrain_query = """ create or replace…

Continue Reading## Practical aspects — Logistic Regression in layman terms

Why we choose this specific cost function (and not a linear function or any other function for that matter) is…

Continue Reading## Machine learning for people who know nothing about machine learning

It does a great job of matching the data we do have, but it won’t be able to make sensible…

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