Linearity

Independence

Normality

Homoscedasticity

Predicting patient outcomes

Identifying patients at high risk for disease

Evaluating treatment effectiveness

Image recognition

To measure the accuracy of the model

To measure the effect of each feature on the outcome

To visualize the decision boundary of the model

To identify the outliers in the data

To evaluate the overall accuracy of the model

To evaluate the precision and recall of the model

To visualize the decision boundary of the model

To identify the outliers in the data

To normalize the output of the linear function

To map the output to a probability value

To reduce the risk of overfitting

To improve the convergence rate of the optimization algorithm

It can only model linear relationships between variables

It is prone to overfitting when there are too many features

It requires large amounts of training data

It is sensitive to outliers

Dropping the rows with missing values

Replacing the missing values with the mean or median

Imputing the missing values using a predictive model

Ignoring the missing values

It can only handle linearly separable data

It is sensitive to outliers in the data

It requires a large amount of training data

It is not suitable for high-dimensional data

To determine the regularization strength of the model

To control the step size in the optimization algorithm

To set the initial values of the model coefficients

To control the number of iterations in the optimization algorithm

Binary logistic regression can only predict two classes, while multinomial logistic regression can predict more than two classes

Binary logistic regression assumes that the classes are independent, while multinomial logistic regression assumes that they are not independent

Binary logistic regression uses a different cost function than multinomial logistic regression

Binary logistic regression requires more training data than multinomial logistic regression