Why logistic regression is not suitable?

Non-linear problems can't be solved with logistic regression because it has a linear decision surface. Linearly separable data is rarely found in real-world scenarios. Good accuracy for many simple data sets and it performs well when the dataset is linearly separable.

When should logistic regression not be used?

4. Logistic Regression should not be used if the number of observations is lesser than the number of features, otherwise, it may lead to overfitting.

Why logistic regression is not a regression problem?

Linear regression gives a continuous value of output y for a given input X. Whereas, logistic regression gives a continuous value of P(Y=1) for a given input X, which is later converted to Y=0 or Y=1 based on a threshold value. That's the reason, logistic regression has “Regression” in its name.

What does logistic regression not do?

Logistic regression does not make many of the key assumptions of linear regression and general linear models that are based on ordinary least squares algorithms – particularly regarding linearity, normality, homoscedasticity, and measurement level.

What kind of problems is a logistic regression model well suited for?

In addition, logistic regression is well suited for problems when the predictor variable is binary or has multiple categorical levels, or even when there are multiple independent variables in the problem. For further reading on logit models, we refer to Maddala (1983) and Greene (1993).

Why Linear Regression is not suitable for Classification?

What are the disadvantages of logistic regression?

The major limitation of Logistic Regression is the assumption of linearity between the dependent variable and the independent variables. It not only provides a measure of how appropriate a predictor(coefficient size)is, but also its direction of association (positive or negative).

Is logistic regression sensitive to outliers?

Like linear regression, estimates of the logistic regression are sensitive to the unusual observations: outliers, high leverage, and influential observations.

Is logistic regression accurate?

Prediction accuracy

The most basic diagnostic of a logistic regression is predictive accuracy.

Is there any assumption in logistic regression?

Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers.

Why do we use logistic regression rather than linear regression?

Linear Regression is used to handle regression problems whereas Logistic regression is used to handle the classification problems. Linear regression provides a continuous output but Logistic regression provides discreet output.

Can logistic regression be used for non linear data?

So to answer your question, Logistic regression is indeed non linear in terms of Odds and Probability, however it is linear in terms of Log Odds.

What are the two main differences between logistic regression and Linear Regression?

Linear Regression is a supervised regression model. Logistic Regression is a supervised classification model. In Linear Regression, we predict the value by an integer number. In Logistic Regression, we predict the value by 1 or 0.

Can we apply logistic regression on a 3 class classification problem explain your answer in less than two lines?

Solution: A

Yes, we can apply logistic regression on 3 classification problem, We can use One Vs all method for 3 class classification in logistic regression.

Which is better logistic regression or decision tree?

If you've studied a bit of statistics or machine learning, there is a good chance you have come across logistic regression (aka binary logit).

Is there an error term in logistic regression?

Logistic Regression is one type of Generalized Linear Model and they all have that same feature. Rather than model each value of Y with the predicted mean plus an error term, it simply models the predicted mean.

Can we use logistic regression for continuous variables?

Logistic regression is usually used with binary response variables ( 0 or 1 ), the predictors can be continuous or discrete.

Is logistic regression non parametric?

The logistic regression model is parametric because it has a finite set of parameters. Specifically, the parameters are the regression coefficients. These usually correspond to one for each predictor plus a constant. Logistic regression is a particular form of the generalised linear model.

Does multicollinearity effects logistic regression?

Multi- collinearity may also result in wrong signs and magnitudes of logistic regression coefficient estimates, and consequently incorrect conclusions about relationships between explanatory and response variables.

What happens if logistic regression assumptions are violated?

Similar to what occurs if assumption five is violated, if assumption six is violated, then the results of our hypothesis tests and confidence intervals will be inaccurate. One solution is to transform your target variable so that it becomes normal. This can have the effect of making the errors normal, as well.

Is logistic regression fast?

Logistic regression is probably the most important supervised learning classification method. It's a fast, versatile extension of a generalized linear model.

Is cross validation necessary for logistic regression?

In general cross validation is always needed when you need to determine the optimal parameters of the model, for logistic regression this would be the C parameter. The goal of CV is not to estimate parameters but to estimate the generalization performance and stability of your full learning procedure.

Which type of dataset is used for logistic regression?

Logistic Regression is a significant machine learning algorithm because it has the ability to provide probabilities and classify new data using continuous and discrete datasets.

Is logistic regression robust?

The classical approach for estimating parameters is the maximum likelihood estimation, a disadvantage of this method is high sensitivity to outlying observations. Robust estimators for logistic regression are alternative techniques due to their robustness.

Why linear regression works poorly with outliers?

It is sensitive to outliers and poor quality data—in the real world, data is often contaminated with outliers and poor quality data. If the number of outliers relative to non-outlier data points is more than a few, then the linear regression model will be skewed away from the true underlying relationship.

Which algorithms are sensitive to outliers?

The K-means clustering algorithm is sensitive to outliers, because a mean is easily influenced by extreme values. K-medoids clustering is a variant of K-means that is more robust to noises and outliers.