Contents

- 1 How do I speed up GridSearchCV?
- 2 What does Gridsearchcv return?
- 3 What are tuning parameters?
- 4 What is C parameter in logistic regression?
- 5 What is Coef_?
- 6 What is C in regression?
- 7 What are the parameters in logistic regression?
- 8 Which algorithm is used to predict continuous values?
- 9 What is difference between linear and logistic regression?
- 10 What is logistic regression algorithm?
- 11 What is the loss function used in logistic regression to find the best fit line?

## How do I speed up GridSearchCV?

You can get an instant 2-3x speedup by switching to 5- or 3-fold CV (i.e., cv=3 in the GridSearchCV call) without any meaningful difference in performance estimation. Try fewer parameter options at each round. With 9×9 combinations, you’re trying 81 different combinations on each run.

## What does Gridsearchcv return?

Returns the score on the given data, if the estimator has been refit. This uses the score defined by scoring where provided, and the best_estimator_. score method otherwise. Input data, where n_samples is the number of samples and n_features is the number of features.

## What are tuning parameters?

A tuning parameter (λ), sometimes called a penalty parameter, controls the strength of the penalty term in ridge regression and lasso regression. It is basically the amount of shrinkage, where data values are shrunk towards a central point, like the mean.

## What is C parameter in logistic regression?

C : float, optional (default=1.0) Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization. What does C mean here in simple terms please?

## What is Coef_?

The coef_ contain the coefficients for the prediction of each of the targets. It is also the same as if you trained a model to predict each of the targets separately.

## What is C in regression?

The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable. Naming the Variables.

## What are the parameters in logistic regression?

The parameters of a logistic regression model can be estimated by the probabilistic framework called maximum likelihood estimation….This tutorial is divided into four parts; they are:Logistic Regression.Logistic Regression and Log-Odds.Maximum Likelihood Estimation.Logistic Regression as Maximum Likelihood.

## Which algorithm is used to predict continuous values?

Regression Techniques Regression algorithms are machine learning techniques for predicting continuous numerical values.

## What is difference between linear and logistic regression?

Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems.

## What is logistic regression algorithm?

Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes. Mathematically, a logistic regression model predicts P(Y=1) as a function of X.

## What is the loss function used in logistic regression to find the best fit line?

Logistic regression models generate probabilities. Log Loss is the loss function for logistic regression. Logistic regression is widely used by many practitioners.