Machine Learning Basic Interview Questions

 

Certainly! Here are 10 more machine learning interview questions along with their answers in regards to previous LinkedIn post:

Certainly! Here are 10 more machine learning interview questions along with their answers:

21. Explain the concept of Support Vector Machines (SVM).

    - Answer: Support Vector Machines are supervised learning models used for classification and regression tasks. SVM finds a hyperplane that best separates different classes by maximizing the margin between them.

22.   What is the purpose of the F1 score?  

    -  Answer:  The F1 score is the harmonic mean of precision and recall. It provides a balance between precision and recall, making it a useful metric when the class distribution is imbalanced.

23.   Describe the concept of Cross-Entropy in the context of Neural Networks.  

    -  Answer:  Cross-Entropy is a loss function used in neural networks for classification tasks. It measures the dissimilarity between predicted probability distributions and true distributions, encouraging the model to make confident and accurate predictions.

24.   What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?  

    -  Answer:  In Batch Gradient Descent, the model parameters are updated based on the gradients computed over the entire training dataset. In Stochastic Gradient Descent, the updates are performed on each individual data point, making it computationally more efficient but leading to more frequent updates.

25.   Explain the concept of Ensemble Learning.  

    -  Answer:  Ensemble Learning involves combining multiple machine learning models to improve overall performance and accuracy. Common techniques include Bagging (e.g., Random Forests) and Boosting (e.g., AdaBoost, Gradient Boosting).

26.   What is the purpose of a ROC Curve?  

    -  Answer:  The Receiver Operating Characteristic (ROC) Curve is a graphical representation of a classifier's performance across different thresholds. It plots the true positive rate against the false positive rate, providing insights into a model's ability to discriminate between classes.

27.   Describe the concept of Dropout in Neural Networks.  

   -  Answer:  Dropout is a regularization technique used in neural networks to prevent overfitting. It randomly drops a proportion of neurons during training, forcing the network to learn more robust and generalizable features.

28.   What is the Curse of Dimensionality?  

   -  Answer:  The Curse of Dimensionality refers to the challenges and limitations that arise when dealing with high-dimensional data. As the number of features increases, the amount of data needed to cover the feature space adequately grows exponentially, leading to sparsity and increased computational complexity.

29.   Explain the concept of Reinforcement Learning.  

    -  Answer:  Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or punishments, guiding it to optimize its behavior over time.

30.   What is the difference between Type I and Type II errors?  

    -  Answer:  Type I error occurs when a true null hypothesis is incorrectly rejected, while Type II error occurs when a false null hypothesis is not rejected. In the context of machine learning, Type I error is often referred to as a false positive, and Type II error is a false negative.