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Regression


Sentiment Analysis using Logistic Regression and Naive Bayes

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In supervised machine learning, you usually have an input X, which goes into your prediction function to get your Y . You can then compare your prediction with the true value Y. This gives you your cost which you use to update the parameters θ. Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. So, let's start sentiment analysis using Logistic Regression We will be using the sample twitter data set for this exercise.


Logistic Regression for Binary Classification

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In previous articles, I talked about deep learning and the functions used to predict results. In this article, we will use logistic regression to perform binary classification. Binary classification is named this way because it classifies the data into two results. Simply put, the result will be "yes" (1) or "no" (0). To determine whether the result is "yes" or "no", we will use a probability function: This probability function will give us a number from 0 to 1 indicating how likely this observation will belong to the classification that we have currently determined to be "yes".


Simple Linear Regression Tutorial for Machine Learning (ML)

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Simple linear regression is a statistical approach that allows us to study and summarize the relationship between two continuous quantitative variables. Simple linear regression is used in machine learning models, mathematics, statistical modeling, forecasting epidemics, and other quantitative fields. Out of the two variables, one variable is called the dependent variable, and the other variable is called the independent variable. Our goal is to predict the dependent variable's value based on the value of the independent variable. A simple linear regression aims to find the best relationship between X (independent variable) and Y (dependent variable).


Linear Regression for Dummies

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In my previous article, I have highlighted 4 algorithms to start off in Machine Learning: Linear Regression, Logistic Regression, Decision Trees and Random Forest. Now, I am creating a series of the same. The equation which defines the simplest form of the regression equation with one dependent and one independent variable: y mx c. Where y estimated dependent variable, c constant, m regression coefficient and x independent variable. Let's just understand with an example: Say; There is a certain relationship between the marks scored by the students (y- Dependent variable) in an exam and hours they studied for the exam(x- Independent Variable).


Linear Regression and Logistic Regression using R Studio

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In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


Regression Vs Classification In Machine Learning

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Regression and classification are many times confusing to many beginners in the field of Machine learning. Eventually, this will make it impossible for them to adopt the correct methodologies for solving problems with prediction. Regression and classification are both types of supervised machine learning algorithms, where a model is trained along with correctly labeled data according to the current model. Let's understand each algorithm first. Regression algorithms estimate a continuous value based on the input variables.


Creating an Algorithmic Trading Strategy Using Python and Logistic Regression

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Obtaining historical data on the stocks that we want to observe is a two-step process. The library get-all-tickers allows us to compile a list of stock tickers by filtering companies on aspects like market cap or exchange. For this example, I am looking at companies that have a market cap between $150,000 and $10,000,000 (in millions). You will notice that I also included a line of code to print the number of tickers we are using. You will need to be sure that you are not targeting more than 2,000 tickers, because the Yfinance API has a 2,000 API calls per hour limit.


Intro to Regularization With Ridge And Lasso Regression with Sklearn

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Ordinary Least Squares is one of the easiest and most widely used ML algorithms. But it suffers from a fatal flaw -- it is super easy for the algorithm to overfit the training data. With large coefficients, it is easy to predict nearly everything -- you just take the relevant combination of individual slopes (βs) and you get the answer. That's why it is common for linear regression models to overfit the training data. Another problem with LR is that it does not care about the weights of the features.


Bayesian Thinking & Estimating Posterior Distribution for Linear Regression @ Data Ketchup…

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One of the major motivations of this research is the fact that there has been an increasing focus on Deep model interpretability with the advent of more and more complex models. More is the complexity of the model, difficult it gets to have interpretability with respect to the outputs and a lot of research is going in the field of Bayesian thinking and learning. But before understanding and being able to appreciate Bayesian in deep neural models, we should be well versed and adept with Bayesian thinking in linear models for example- Bayesian Linear regression. But there are very few good materials available online in a combined fashion which can give a clear motivation and understanding of the Bayesian Linear regression. This was one of the major motivations for this blog and here I will try to give an understanding of how to approach the Linear regression from a Bayesian analysis standpoint.


ML for Business Managers: Build Regression model in R Studio

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In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.