- How do you explain linear regression in interview?
- How do you import logistic regression?
- Why linear regression is not suitable for classification?
- What types of data do we model with logistic regression?
- Which model is widely used for classification?
- Which algorithm is best for multiclass classification?
- Is K means a classification algorithm?
- What is difference between linear and logistic regression?
- Where is logistic regression used?
- Why is logistic regression better?
- Which methods do we use to best fit the data in logistic regression?
- Which of the following methods do we use to find the best fit line for data in linear regression?
- Which of these methods is used for fitting a logistic regression model using Statsmodels?
- Which one is a classification algorithm?
- How do you tell if a regression line is a good fit?
- How do you implement logistic regression from scratch?
- Which algorithm is best for text classification?

## How do you explain linear regression in interview?

What is linear regression.

In simple terms, linear regression is a method of finding the best straight line fitting to the given data, i.e.

finding the best linear relationship between the independent and dependent variables..

## How do you import logistic regression?

Logistic Regression in Python With StatsModels: ExampleStep 1: Import Packages. All you need to import is NumPy and statsmodels.api : … Step 2: Get Data. You can get the inputs and output the same way as you did with scikit-learn. … Step 3: Create a Model and Train It.

## Why linear regression is not suitable for classification?

There are two things that explain why Linear Regression is not suitable for classification. The first one is that Linear Regression deals with continuous values whereas classification problems mandate discrete values. The second problem is regarding the shift in threshold value when new data points are added.

## What types of data do we model with logistic regression?

Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.

## Which model is widely used for classification?

The periodic tableThe periodic table is the most widely used and accepted classification table worldwide.

## Which algorithm is best for multiclass classification?

Popular algorithms that can be used for multi-class classification include:k-Nearest Neighbors.Decision Trees.Naive Bayes.Random Forest.Gradient Boosting.Apr 8, 2020

## Is K means a classification algorithm?

KMeans is a clustering algorithm which divides observations into k clusters. Since we can dictate the amount of clusters, it can be easily used in classification where we divide data into clusters which can be equal to or more than the number of classes.

## What is difference between linear and logistic regression?

Linear regression is used to predict the continuous dependent variable using a given set of independent variables. Logistic Regression is used to predict the categorical dependent variable using a given set of independent variables. Linear Regression is used for solving Regression problem.

## Where is logistic regression used?

3 Logistic regression. Logistic regression analysis is used to examine the association of (categorical or continuous) independent variable(s) with one dichotomous dependent variable. This is in contrast to linear regression analysis in which the dependent variable is a continuous variable.

## Why is logistic regression better?

Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space.

## Which methods do we use to best fit the data in logistic regression?

Just as ordinary least square regression is the method used to estimate coefficients for the best fit line in linear regression, logistic regression uses maximum likelihood estimation (MLE) to obtain the model coefficients that relate predictors to the target.

## Which of the following methods do we use to find the best fit line for data in linear regression?

Line of best fit refers to a line through a scatter plot of data points that best expresses the relationship between those points. Statisticians typically use the least squares method to arrive at the geometric equation for the line, either though manual calculations or regression analysis software.

## Which of these methods is used for fitting a logistic regression model using Statsmodels?

Statsmodels provides a Logit() function for performing logistic regression. The Logit() function accepts y and X as parameters and returns the Logit object. The model is then fitted to the data.

## Which one is a classification algorithm?

3.1 Comparison MatrixClassification AlgorithmsAccuracyF1-ScoreNaïve Bayes80.11%0.6005Stochastic Gradient Descent82.20%0.5780K-Nearest Neighbours83.56%0.5924Decision Tree84.23%0.63083 more rows•Jan 19, 2018

## How do you tell if a regression line is a good fit?

The closer these correlation values are to 1 (or to –1), the better a fit our regression equation is to the data values. If the correlation value (being the “r” value that our calculators spit out) is between 0.8 and 1, or else between –1 and –0.8, then the match is judged to be pretty good.

## How do you implement logistic regression from scratch?

We’ve accomplished our second objective which is to implement a Logistic Regression without the help of built-in libraries (except numpy of course). Log Regression from scratch using loss minimization. Log Regression from scratch using maximum likelihood estimation. Log Regression class of sklearn.

## Which algorithm is best for text classification?

Naive BayesThe Naive Bayes family of statistical algorithms are some of the most used algorithms in text classification and text analysis, overall.