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I applied via LinkedIn and was interviewed before Apr 2023. There was 1 interview round.
fbprophet is a forecasting model developed by Facebook that uses time series data to make predictions.
fbprophet is an open-source forecasting tool developed by Facebook's Core Data Science team.
It is based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
fbprophet can be used to forecast traffic by providing historical data on traffic patterns and usi...
I applied via Company Website and was interviewed in Jul 2024. There were 5 interview rounds.
posted on 21 Oct 2022
I applied via Approached by Company and was interviewed in Sep 2022. There were 3 interview rounds.
I applied via Approached by Company and was interviewed in Aug 2023. There was 1 interview round.
Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.
One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.
Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.
Evaluate the model using appropriate...
I applied via Recruitment Consulltant and was interviewed in Oct 2024. There was 1 interview round.
45 mins.
questions based on basic string manipulation but not a straightforward task
I applied via Company Website and was interviewed before Mar 2023. There was 1 interview round.
L1 & L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty term to the cost function.
L1 regularization adds the absolute values of the coefficients as penalty term (Lasso regression)
L2 regularization adds the squared values of the coefficients as penalty term (Ridge regression)
L1 regularization encourages sparsity in the model, while L2 regularization tends to shrink the c...
Error metric is a measure used to evaluate the performance of a model by comparing predicted values to actual values.
Error metric quantifies the difference between predicted values and actual values.
Common error metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared.
Lower values of error metric indicate better performance of the model.
Error metric helps in und...
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Oracle
Amdocs
Carelon Global Solutions
Automatic Data Processing (ADP)