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When can you not use logistic regression?
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 hypothesis is used for logistic regression?
null hypothesis
In logistic regression, two hypotheses are of interest: the null hypothesis, which is when all the coefficients in the regression equation take the value zero; and the alternative hypothesis, that the model with predictors currently under consideration is accurate and differs significantly from the null or zero.
Which is better logistic regression or linear regression?
Let’s compare the R² and RMSE again for both models, and you will see that logistic regression does a way better job than linear regression. Linear regression is suitable for predicting output that is continuous value, such as predicting the price of a property. Its prediction output can be any real number, range from negative infinity to infinity.
What are the assumptions of a linear regression model?
There are four principal assumptionswhich justify the use of linear regression models for purposes of inference or prediction: (i) linearityand additivityof the relationship between dependent and independent variables:
What do you need to know about multiple linear regression?
1. Linear relationship: There exists a linear relationship between the independent variable, x, and the dependent variable, y. 2. Independence: The residuals are independent. In particular, there is no correlation between consecutive residuals in time series data. 3. Homoscedasticity: The residuals have constant variance at every level of x.
When to use log transformation in linear regression?
If a log transformation is applied to boththe dependent variable and the independent variables, this is equivalent to assuming that the effects of the independent variables are multiplicativerather than additive in their original units.