Horlicks Interview Questions and Answers

My final year project was on logistic regression.

• Logistic regression is a statistical model used to predict binary outcomes.

• It is a type of regression analysis where the dependent variable is categorical.

• The model estimates the probability of the outcome based on the input variables.

• It uses a logistic function to map the predicted values to probabilities.

• The model is commonly used in various fields like finance, marketing, and healthcare.

• For example, logistic regression can b...read more

Hypothesis testing is a statistical method to test a hypothesis about a population parameter. P-value is the probability of observing a test statistic as extreme as the one computed from the sample data, assuming the null hypothesis is true.

• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis

• A test statistic is computed from the sample data and compared with a critical value or p-value

• If the p-value is less than the significance level, the n...read more

Machine learning is a subset of AI that involves training machines to learn from data. Deep learning is a subset of machine learning that uses neural networks to learn from large amounts of data.

• Machine learning involves training machines to learn from data and make predictions or decisions based on that data.

• Deep learning is a subset of machine learning that uses neural networks to learn from large amounts of data.

• Deep learning is particularly useful for tasks such as image ...read more

We use sample over population to save time, money and resources.

• Sampling is less time-consuming and cost-effective than studying the entire population.

• Sampling helps to reduce errors and biases that may occur in studying the entire population.

• Sampling is useful when the population is too large to study in its entirety.

• Sampling can provide accurate results if the sample is representative of the population.

Bias is the difference between predicted values and actual values, while variance is the variability of model predictions.

• Bias is the error caused by oversimplification of the model, while variance is the error caused by overcomplication of the model.

• High bias leads to underfitting, while high variance leads to overfitting.

• Bias and variance are inversely proportional, meaning reducing one increases the other.

• An example of high bias is a linear regression model for a non-linea...read more

Oops concepts are fundamental principles of object-oriented programming. getWindowHandle() returns the handle of the current window, while getWindowHandles() returns the handles of all open windows.

• Oops concepts include inheritance, polymorphism, encapsulation, and abstraction.

• Inheritance: Reusing code from a parent class in a child class.

• Polymorphism: Objects of different classes can be treated as objects of a common superclass.

• Encapsulation: Bundling data and methods that o...read more

Data is shared between components through props and state in React.

• In React, data is passed down from parent components to child components through props.

• Components can also have their own state which can be updated and passed down to child components.

• Redux can also be used for managing global state and sharing data between components.

• In Angular, data can be shared between components through services or by using @Input and @Output decorators.

• Vue.js uses props and events to sh...read more