Quick question from data science and machine learning interview | Part 5

1. Explain Gradient Descent algorithm.

Ans. Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. Parameters refer to coefficients in Linear Regression and weights in neural networks.

2. What is logistic regression used for classification instead of linear regression?

Ans. Using linear Regression , all predictions >= 0.5 can be considered as 1 and rest all < 0.5 can be considered as 0. But then the question arises why classification can’t be performed using it? Suppose we are classifying a mail as spam or not spam and our output is y, it can be 0(spam) or 1(not spam). In case of Linear Regression, hθ(x) can be > 1 or < 0. Although our prediction should be in between 0 and 1, the model will predict value out of the range i.e. maybe > 1 or < 0. So, that’s why for a Classification task, Logistic/Sigmoid Regression plays its role.

3. What is the Gini Index?

Ans. Gini Index is a score that evaluates how accurate a split is among the classified groups. Gini index evaluates a score in the range between 0 and 1, where 0 is when all observations belong to one class, and 1 is a random distribution of the elements within classes. In this case, we want to have a Gini index score as low as possible. Gini Index is the evaluation metrics we shall use to evaluate our Decision Tree Model.

4. Why is DBSCAN used over K means and other clustering methods?

Ans. Partitioning methods (K-means, PAM clustering) and hierarchical clustering work for finding spherical-shaped clusters or convex clusters. In other words, they are suitable only for compact and well-separated clusters. Moreover, they are also severely affected by the presence of noise and outliers in the data.

Real life data may contain irregularities, like:

• Clusters can be of arbitrary shape like non convex clusters
• Data may contain noise.
• Given such data, k-means algorithm has difficulties in identifying these clusters with arbitrary shapes.

ENJOY LEARNING ????