Answer / Manorma Sharma

Bias and variance are two key concepts in machine learning that describe the error of a model. Bias refers to the systematic error caused by an overly simplistic or wrong assumption about the relationship between variables, leading to underfitting or overestimation of the training data. Variance, on the other hand, is the random error caused by sensitive dependence on the training set, leading to overfitting or underestimation of the test data. A good model has low bias and low variance, allowing it to generalize well to unseen data.

What is symbolic processing?

1 Answers  

---

What’s the trade-off between bias and variance?

1 Answers  

---

What is machine learning artificial intelligence?

1 Answers  

---

Explain how do you ensure you're not overfitting with a model?

1 Answers  

---

What are some differences between a linked list and an array?

1 Answers  

---

Explain the importance of bayes theorem?

1 Answers  

---

You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why ?

1 Answers  

---

What is symbolic reasoning?

1 Answers  

---

Explain the Algorithm of Nearest Neighbor in Machine Learning?

1 Answers  

---

What is conditional probability?

1 Answers  

---

What is the standard approach to supervised learning?

1 Answers  

---

Explain the Algorithm of Probabilistic networks in Machine Learning?

1 Answers  

---