Question

Based on a cursory analysis of the data, logistic regression looks like a good algorithm to use. Explain why logistic regression could be a good choice for this data set. Provide a high level overview of how logistic regression works. Explain what feature engineering tasks need to be performed. If additional analysis shows that the data are not linearly separable, what other algorithms would you recommend and why?

Answer 1

Logistic regression could be a good algorithm to use for this data set because it is commonly used for binary classification problems. Feature engineering tasks need to be performed to improve the performance of logistic regression. If the data are not linearly separable, other algorithms that can be recommended include support vector machines, random forests, and gradient boosting.

Step-by-step explanation:

Logistic regression could be a good algorithm to use for this data set because it is commonly used for binary classification problems, where the target variable has two possible outcomes. Logistic regression estimates the probability of the target variable belonging to a certain class, based on the input features. It can handle both numerical and categorical features and works well with large data sets.

Logistic regression works by fitting a logistic curve to the data, which is a sigmoid function that maps the input features to the probability of the target variable being in one of the classes. The algorithm uses maximum likelihood estimation to find the optimal parameters that minimize the error between the predicted probabilities and the actual classes.

Feature engineering tasks need to be performed to improve the performance of logistic regression. These tasks involve transforming the input features to better represent the underlying patterns in the data. Some common feature engineering tasks for logistic regression include one-hot encoding for categorical variables, scaling numerical variables, handling missing values, and creating interaction terms.

If additional analysis shows that the data are not linearly separable, other algorithms that can be recommended include support vector machines (SVM), random forests, and gradient boosting. SVM can create non-linear decision boundaries by using different kernel functions. Random forests and gradient boosting are ensemble methods that combine multiple decision trees to make predictions. These algorithms can handle non-linear relationships between the features and the target variable.