Infosys Machine Learning Engineer Interview Questions, Process, and Tips

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 p...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 know...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).

I am a dedicated and passionate Machine Learning Engineer with a strong background in computer science and data analysis.

• Experienced in developing machine learning models for various applications

• Proficient in programming languages such as Python, R, and Java

• Skilled in data preprocessing, feature engineering, and model evaluation

• Strong understanding of algorithms and statistical concepts

• Excellent problem-solving and ana

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 regul...read more

AUC (Area Under the Curve) is a metric that measures the performance of a classification model. ROC (Receiver Operating Characteristic) is a graphical representation of the AUC.

• AUC is a single scalar value that represents the area under the ROC curve.

• ROC curve is a plot of the true positive rate against the false positive rate for different threshold values.

• AUC ranges from 0 to 1, where a higher value indicates better ...read more

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

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,