Mentora Institute Interview Questions and Answers

Find students who scored more than avg marks in both 11th and 12th grades.

• Calculate the average marks for each student in 11th and 12th grades.

• Compare each student's marks with the respective average marks to find those who scored higher in both grades.

Use a SQL query to find customers who have ordered all products from all categories.

• Join the Customers, Orders, and Products tables

• Group by customer and count the distinct products ordered

• Filter for customers who have ordered the total number of products available in each category

Using linear regression for classification can lead to inaccurate predictions and unreliable results.

• Linear regression assumes a continuous output, making it unsuitable for discrete classification tasks.

• It may not handle outliers well, leading to incorrect classification boundaries.

• The predicted values may fall outside the 0-1 range for binary classification.

• Logistic regression is a more appropriate choice for classification tasks.

GAN stands for Generative Adversarial Network, a type of neural network used for generating new data.

• Consists of two neural networks - generator and discriminator

• Generator creates new data samples while discriminator tries to distinguish between real and generated data

• Used in image generation, text generation, and other creative applications

Generate all possible subsets of a given list in Python.

• Use itertools.combinations to generate all possible combinations of the list elements.

• Convert the combinations to lists and store them in a new list to get all subsets.

Cost function is a mathematical function that measures the error between predicted values and actual values in a machine learning model.

• Cost function helps in optimizing the parameters of a model to minimize the error.

• Common cost functions include Mean Squared Error (MSE) and Cross Entropy Loss.

• It is used in training machine learning models through techniques like gradient descent.

• The goal is to find the parameters that minimize the cost function.

Gini coefficient is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents.

• Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.

• A Gini coefficient of 0.4 is considered moderate inequality, while 0.6 or higher is considered high inequality.

• It is commonly used in economics to measure income inequality within a population.

• The formula for calculating Gini coefficie...read more

Feature engineering is crucial in data science as it involves selecting, transforming, and creating new features to improve model performance.

• Feature engineering helps in improving model accuracy by providing relevant and meaningful input variables.

• It involves techniques like one-hot encoding, scaling, normalization, and creating interaction terms.

• Feature engineering can help in reducing overfitting and improving model interpretability.

• Examples include creating new features f...read more