Feynn Labs Data Science Intern Interview Questions and Answers

Q1. What is random forest? What it is called random?
Ans. 
Random forest is an ensemble learning method used for classification and regression tasks, consisting of multiple decision trees.
• Random forest is made up of multiple decision trees, where each tree is built using a subset of the training data and a random subset of features.

• During prediction, each tree in the forest independently predicts the output, and the final output is determined by a majority vote (classification...read more
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Q2. What is svm? Any project you perform using this?
Ans. 
SVM stands for Support Vector Machine, a supervised machine learning algorithm used for classification and regression tasks.
• SVM finds the hyperplane that best separates different classes in the feature space.

• It can handle both linear and non-linear data by using different kernel functions.

• Example project: Sentiment analysis using SVM to classify movie reviews as positive or negative.
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Q1. Difference between Random and ordering partition
Ans. 
Random partition involves splitting data randomly, while ordering partition involves splitting data based on a specific order.
• Random partition randomly divides data into subsets without any specific order.

• Ordering partition divides data into subsets based on a specific order, such as time or alphabetical order.

• Random partition is useful for creating training and testing sets for machine learning models.

• Ordering partiti...read more
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Q2. Difference between Logistic and Linear Regression
Ans. 
Logistic regression is used for binary classification while linear regression is used for regression tasks.
• Logistic regression predicts the probability of a binary outcome (0 or 1) based on one or more independent variables.

• Linear regression predicts a continuous outcome based on one or more independent variables.

• Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regressio...read more
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Q3. Difference between KNN and K Means
Ans. 
KNN is a supervised learning algorithm used for classification and regression, while K Means is an unsupervised clustering algorithm.
• KNN stands for K-Nearest Neighbors and assigns a class label based on majority voting of its k-nearest neighbors.

• K Means is a clustering algorithm that partitions data into k clusters based on similarity.

• KNN requires labeled data for training, while K Means does not need labeled data.

• KNN ...read more
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Q4. Range of Cross Entropy Loss
Ans. 
Cross entropy loss measures the difference between two probability distributions.
• Range of cross entropy loss is [0, infinity)

• Lower values indicate better model performance

• Commonly used in classification tasks
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Q1. Introduced yourself.?
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Q2. What is linear and logistics.?
Ans. 
Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.
• Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.

• In linear regression, the relationship between the independent and dependent variables...read more
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