Regression


R-Squared Explained for Indian Grandma - Reskilling IT

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In this post, you will learn about the concept of R-Squared in relation to assess the performance of multilinear regression machine learning model with the help of some real-world examples explained in simple manner. Once we have built a multilinear regression model, the next thing is to find out the model performance. The model performance can be found out by calculating the value of the Residual Standard Error (RSE) or the value of R-Squared. Residual Standard Error can be defined as the difference between the mean value of the prediction made by the model and the population mean value. In this article, we will learn the technique of evaluating the model performance using the value of R-Squared.


Reflection on modern methods: when worlds collide--prediction, machine learning and causal inference

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Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs.


Heart of Darkness: Logistic Regression vs. Random Forest

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The'functional needs repair' category of the target variable only makes up about 7% of the whole set. The implication is that whatever algorithm you end up using it's probably going to learn the other two balanced classes a lot better than this one. Such is data science: the struggle is real. The first thing we're going to do is create an'age' variable for the waterpoints as that seems highly relevant. The'population' variable also has a highly right-skewed distribution so we're going to change that as well: One of the most important points we learned from the week before and something that will stay with me is the idea of coming up with a baseline model as fast as one can.


Predicting Cancer with Logistic Regression in Python

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Let's jump into the analysis by pulling in the data and importing necessary modules. Each row is a patient and each column contains a descriptive attribute. Class (Y) describes if the patient has no cancer (0) or has cancer (1). The next 4 columns are the protein levels found in that patient's bloodstream. We can retrieve some basic information about the sample from the describe method.


Essential Machine Learning with Linear Models in RAPIDS: Part 1 of a Series

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I want to take a moment to tell the origin story of regression analysis, which will explain why it has that name. I believe that of all the common machine learning techniques (K-means, kNN, PCA), "regression analysis" has the most opaque name. OLS regression was first invented to analyze exceptional genetic traits and their heritability. These early studies seemed to show the offspring of exceptional individuals "regressed to the mean". The inventor was Sir Francis Galton (half-cousin of Charles Darwin²), who had previously invented the standard deviation and first observed the "wisdom of the crowds" in certain estimation tasks. I am trying to predict daily demand for short-term bike rentals made in 2012, and I have data from 2011 to build the model.


Machine Learning Dates Back To at Least 300 BC

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Many people think that artificial intelligence and machine learning are recent phenomena. However, these techniques and ideas actually go back deep into human history. Machine learning has always been an important tool for data mining for humanity, it was given different names in different eras. The key to machine learning is not machines but mathematics. There is nothing special about silicon and electricity.


Chatbots aren't as difficult to make as You Think

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Ok, so finally we are at a stage where we can do something we love. Use Data Science to power our Application/Chatbot. Let us start with creating a rough architecture of what we are going to do next. We will need to create two classifiers and save them as .pkl To keep it simple we will create simple TFIDF models.


Comprehensive Guide To Logistic Regression In R Edureka

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A logistic regression model is said to be statistically significant only when the p-Values are less than the pre-determined statistical significance level, which is ideally 0.05. The p-value for each coefficient is represented as a probability Pr( z). We see here that both the coefficients have a very low p-value which means that both the coefficients are essential in computing the response variable. The stars corresponding to the p-values indicate the significance of that respective variable. Since in our model, both the p values have a 3 star, this indicates that both the variables are extremely significant in predicting the response variable.


3.1. Linear Regression -- Dive into Deep Learning 0.7 documentation

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To keep things simple, we will start with running example in which we consider the problem of estimating the price of a house (e.g. in dollars) based on area (e.g. in square feet) and age (e.g. in years). In economics papers, it is common for authors to write out linear models in this format with a gigantic equation that spans multiple lines containing terms for every single feature. For the high-dimensional data that we often address in machine learning, writing out the entire model can be tedious. In these cases, we will find it more convenient to use linear algebra notation. Above, the vector \(\mathbf{x}\) corresponds to a single data point.


Step-By-Step: Getting Started with Azure Machine Learning

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Artificial Intelligence (AI) study and use is on the rise. Tools to enable AI are becoming more readily available, simpler to use and easier to implement. What's more is that the definition of AI itself has been broken down into ingredients that, when later applied into a recipe (or process), can provide multiple desired outcomes. One of the more important ingredients used in most recipes is Machine Learning. Machine Learning in essence is a way of teaching computers to provide more accurate predictions on provided data.