Utopus Insights Data Scientist Interview Questions and Answers

Multicollinearity can be treated by using techniques like feature selection, PCA, or regularization. Imbalanced datasets can be addressed by resampling techniques like oversampling or undersampling.

• For multicollinearity, consider using techniques like feature selection to remove redundant variables, PCA to reduce dimensionality, or regularization like Lasso or Ridge regression.

• For imbalanced datasets, try resampling te...read more

Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.

• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.)

• It estimates the probability that a given observation belongs to a particular category.

• The output of logistic regression is a probability score between 0 and 1.

• It uses the logistic function...read more

Understanding outliers, missing values, overfitting, and underfitting in data science.

• Outliers are data points that significantly differ from other data points in a dataset.

• Missing values are data points that are not present in a dataset.

• Overfitting occurs when a model learns noise in the training data rather than the underlying pattern.

• Underfitting occurs when a model is too simple to capture the underlying pattern in...read more

Multicollinearity can be treated by using techniques like feature selection, PCA, or regularization. Imbalanced datasets can be addressed by resampling techniques like oversampling or undersampling.

• For multicollinearity, consider using techniques like feature selection to remove redundant variables, PCA to reduce dimensionality, or regularization like Lasso or Ridge regression.

• For imbalanced datasets, try resampling te...read more

Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.

• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.)

• It estimates the probability that a given observation belongs to a particular category.

• The output of logistic regression is a probability score between 0 and 1.

• It uses the logistic function...read more

Understanding outliers, missing values, overfitting, and underfitting in data science.

• Outliers are data points that significantly differ from other data points in a dataset.

• Missing values are data points that are not present in a dataset.

• Overfitting occurs when a model learns noise in the training data rather than the underlying pattern.

• Underfitting occurs when a model is too simple to capture the underlying pattern in...read more