1. Difference between Correlation and Regression.
The main difference in correlation vs regression is that the measures of the degree of a relationship between two variables; let them be x and y. Here, correlation is for the measurement of degree, whereas regression is a parameter to determine how one variable affects another.
2. Why do we square the residuals instead of using modulus?
It is because of the extra penalty for higher errors and squaring the residuals for mean deviation were observed to be more efficient than mean absolute deviation.
3. Which evaluation metric should you prefer to use for a dataset having a lot of outliers in it?
Mean Absolute Error(MAE) is preferred when we have too many outliers present in the dataset because MAE is robust to outliers whereas MSE and RMSE are very susceptible to outliers and these start penalizing the outliers by squaring the error terms.
4. Heteroscedasticity? How to detect it?
Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term). To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically.
A p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
6. Root Cause Analysis?
Root cause analysis (RCA) is defined as a collective term that describes a wide range of approaches used to uncover causes of problems. Some RCA approaches are geared more toward identifying true root causes than others.
Regularization is the process which regularizes or shrinks the coefficients towards zero. In simple words, regularization discourages learning a more complex or flexible model, to prevent overfitting.
8. DBSCAN Clustering?
Density-based spatial clustering of applications with noise (DBSCAN) is a well-known data clustering algorithm that is commonly used in data mining and machine learning. The main idea behind DBSCAN is that a point belongs to a cluster if it is close to many points from that cluster. It is able to find arbitrary shaped clusters and clusters with noise (i.e. outliers)