Cyient Interview Questions and Answers

Evaluation metrics for classification are used to assess the performance of a classification model.

• Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

• Accuracy measures the proportion of correctly classified instances out of the total instances.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positive instances.

• F1 score i...read more

p, d, q values are parameters used in ARIMA time series models to determine the order of differencing and moving average components.

• p represents the number of lag observations included in the model (autoregressive order)

• d represents the degree of differencing needed to make the time series stationary

• q represents the number of lagged forecast errors included in the model (moving average order)

• For example, in an ARIMA(1,1,1) model, p=1, d=1, q=1

L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting by adding penalty terms to the loss function.

• L1 regression adds the absolute values of the coefficients as penalty term (Lasso regression)

• L2 regression adds the squared values of the coefficients as penalty term (Ridge regression)

• L1 regularization can lead to sparse models with some coefficients being exactly zero

• L2 regularization generally results in smaller coefficients but no...read more

L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting.

• L1 regression adds a penalty equivalent to the absolute value of the magnitude of coefficients.

• L2 regression adds a penalty equivalent to the square of the magnitude of coefficients.

• L1 regularization can lead to sparse models, while L2 regularization tends to shrink coefficients towards zero.

• L1 regularization is also known as Lasso regression, while L2 regularization is known as...read more

Random forest is an ensemble learning algorithm that builds multiple decision trees and combines their predictions.

• Random forest creates multiple decision trees using bootstrapping and feature randomization.

• Each tree in the random forest is trained on a subset of the data and features.

• The final prediction is made by averaging the predictions of all the trees (regression) or taking a majority vote (classification).

Parameter of random forest is the number of trees in the forest.

• Number of trees in the forest affects model performance

• Higher number of trees can lead to overfitting

• Commonly tuned parameter in random forest algorithms