Linear regressionSuppose you have a linear regression with a couple predictors and no intercept term:β1×1 + β2×2 = y +…
Continue Readingregression
Gradient Descent for Machine Learning
We can use the same equation in order to represent the regression line in computer. If you can’t recall it,…
Continue ReadingUnderstanding Logistic Regression step by step
Understanding Logistic Regression step by stepTraining a logistic regression classifier to predict people’s gender based on their weight and height. Gustavo ChávezBlockedUnblockFollowFollowingFeb…
Continue ReadingIntro to Statistics — Looking at Data
’)# Linear Regressiongradient, intercept, r_value, p_value, std_err = stats. linregress(size,cost)print “Gradient and intercept”, gradient, interceptprint “R-squared”, r_value**2print “p-value”, p_valueprint “Standard…
Continue ReadingMachine Learning Models: Linear Regression
Machine Learning Models: Linear RegressionIsaiah NieldsBlockedUnblockFollowFollowingFeb 1Over the last few weeks, I have been quickly studying my way through Deep…
Continue ReadingLogistic 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 ReadingDealing 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 ReadingUnderstanding 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 ReadingLinear 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 ReadingML 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 ReadingExploring, 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 ReadingHierarchical 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 ReadingFitting 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 ReadingSupervised 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 ReadingIntroduction 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 ReadingHow 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 ReadingInterpreting 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 ReadingRegression 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 ReadingA 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 ReadingRegression 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 ReadingA 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 ReadingAttempting 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 ReadingIntuitions 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 ReadingSimply 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 ReadingA 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