Interview QuestionBank

Explain the notions of overfitting and underfitting in machine learning, and discuss why they are crucial considerations in the building of models.

What would you consider the primary challenges in training deep neural networks with many layers?

What methods do you apply to assess the accuracy of computer vision algorithms?

Could you outline the process of calculating the correlation between a binary variable and a continuous variable?

How do you tackle the issue of high cardinality within categorical data fields?

In your opinion, how does Rectified Linear Unit perform as an activation function?

What constitutes the practice of outlier detection in the field of analytics?

How do you use cross-validation to evaluate the performance of a machine learning model?

What's the objective of employing regularization in machine learning practices?

Describe how a 1D CNN works.

Can you explain how you tackle data skew in model performance evaluations and the metrics that are useful in such situations?

Can you elucidate on the curse of dimensionality and provide a solution you'd employ against it?

When confronted with a high-dimensional dataset in a machine learning problem, what would be your plan of action?

Can you explain how you tackle data skew in model performance evaluations and the metrics that are useful in such situations?

What do you know about cross-validation, and how might it be utilized in the field of machine learning?

What methods have you found useful for determining the most appropriate value of "k" for a given dataset using the K-means algorithm?

Could you explain the concept behind Support Vector Machine?

What do you know about cross-validation, and how might it be utilized in the field of machine learning?

Can you explain how the DBSCAN algorithm works?

What methods have you found useful for determining the most appropriate value of "k" for a given dataset using the K-means algorithm?