Answer / Preetam Singh

"Cross-validation is a technique used to evaluate the performance of machine learning models and prevent overfitting. It involves dividing the dataset into multiple subsets (folds) and training the model on different combinations of folds while testing it on the remaining fold(s). This process is repeated for each fold, and the average performance across all iterations is calculated to estimate the generalization ability of the model. Common types of cross-validation include k-fold cross-validation (k = 5 or 10) and leave-one-out cross-validation (LOOCV)."n

Explain what is the function of ‘Unsupervised Learning’?

1 Answers  

---

Please explain the concept of a boltzmann machine.

1 Answers  

---

Do you have research experience in machine learning?

0 Answers  

---

What's the f1 score? How would you use it?

1 Answers  

---

How is a decision tree pruned?

1 Answers  

---

What is supervised and unsupervised machine learning?

1 Answers  

---

Explain latent dirichlet allocation (lda).

1 Answers  

---

What are the common ways to handle missing data in a dataset?

1 Answers  

---

What is the method to avoid overfitting?

1 Answers  

---

What is the purpose of a classifier?

1 Answers  

---

What was bayes’ theorem? How was it useful in a machine learning context?

1 Answers  

---

How would you evaluate a logistic regression model?

1 Answers  

---