Machine Learning Interview

What is 'training Set' and 'test Set' in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?

-only in supervised model -training set is given to model to analyze and learn -test set is used to test the accuracy of the model. -70/30, 80/20

Explain the Confusion Matrix with Respect to Machine Learning Algorithms.

a confusion matric os a specific table used to measure the performance of an algorithm. They table has two parameters, actual and predicted. represents how accurate the model is.

How Do You Handle Missing or Corrupted Data in a Dataset?

drop the data completely or replace them with some other value. In pandas: IsNull() or dropna() Fillna()

Explain supervised machine learning.

makes predictions or decisions based on past or labeled data. aka data has an answer or solution given with it. Could be a classification problem(categorical) or a regression problem(continuous)

Explain reinforcement machine learning.

model can learn based on the rewards from its pervious action. A machine or an agent interacts with its environment, performs actions, and learns by a trial-and-error method.

Explain unsupervised machine learning.

no labeled data, a model can identify patterns and relationships from the input data. data isn't complete or clean. finds previously unknown patterns. Gives descriptions of categories but none of the data is actually in categories.

What Is a False Positive and False Negative and How Are They Significant?

false positive- wrongly classified as true flase negative- wrongly classified as false These are represented in the confusion matrix, and are important because it shows what percentage of the time our model is incorrect

What is overfitting, and how can you avoid it?

overfitting is when a model learns the training set too well, taking random fluctuations as concepts. This affects the models ability to generalize. You can aviod by making a more simple model (less variables or parameters, cross-validation

What are the different types of machine learning?

Supervised, unsupervised, and reinforcement.

How Can You Choose a Classifier Based on a Training Set Data Size?

When the training set is small, a model that has a right bias and low variance seems to work better.