Feynn Labs Interview Questions and Answers

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
Answered by AI
Add your answer
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer

Q1. Some basic machine learning algorithms related
Add your answer

Q1. What is supervised learning
Ans. 
Supervised learning is a type of machine learning where a model is trained on labeled data to make predictions or classifications.
• Involves training a model on a dataset with input-output pairs.

• Common algorithms include linear regression, decision trees, and support vector machines.

• Used for tasks like classification (e.g., spam detection) and regression (e.g., predicting house prices).

• The model learns to map inputs to o...read more
Answered by AI
Add your answer
Q2. What are the parameters of machine learning
Ans. 
Machine learning parameters include hyperparameters, model parameters, and training parameters that influence model performance.
• Hyperparameters: Settings that are not learned from the data, e.g., learning rate, batch size.

• Model Parameters: Weights and biases learned during training, e.g., coefficients in linear regression.

• Training Parameters: Settings related to the training process, e.g., number of epochs, optimizatio...read more
Answered by AI
Add your answer

Q1. Introduced yourself.?
Add your answer
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
Answered by AI
Add your answer

Data Science Project

Q1. What is 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 input features.

• Linear regression predicts a continuous value based on input features.

• Logistic regression uses a sigmoid function to map predicted values between 0 and 1.

• Linear regression uses a linear equation to model the relations...read more
Answered by AI
Add your answer
Q2. What are the loss functions
Ans. 
Loss functions are used to measure the difference between predicted values and actual values in machine learning models.
• Loss functions quantify how well a model is performing by comparing predicted values to actual values

• Common loss functions include Mean Squared Error (MSE), Cross Entropy Loss, and Hinge Loss

• Different loss functions are used for different types of machine learning tasks, such as regression or classifi...read more
Answered by AI
Add your answer

Write the code for logistic Regression

Q1. All question on machines learning concept
Add your answer
Q2. Concepts on backward propagation
Add your answer