10+ INFAC Interview Questions and Answers

Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.

• For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.

• For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.

• For SVM, important hyperparameters to tune include kernel type, regularization parameter, and gamma value.

• Grid ...read more

Hyperparameters of XGBoost can be tuned using techniques like grid search, random search, and Bayesian optimization.

• Use grid search to exhaustively search through a specified parameter grid

• Utilize random search to randomly sample hyperparameters from a specified distribution

• Apply Bayesian optimization to sequentially choose hyperparameters based on the outcomes of previous iterations

Hyperparameters are settings that control the behavior of machine learning algorithms.

• Hyperparameters are set before training the model.

• They control the learning process and affect the model's performance.

• Examples include learning rate, regularization strength, and number of hidden layers.

• Optimizing hyperparameters is important for achieving better model accuracy.

Hyperparameters in XGBoost algorithm control the behavior of the model during training.

• Hyperparameters include parameters like learning rate, max depth, number of trees, etc.

• They are set before the training process and can greatly impact the model's performance.

• Example: 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100

Ridge and LASSO regression are both regularization techniques used in linear regression to prevent overfitting by adding penalty terms to the cost function.

• Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).

• LASSO regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).

• Ridge regression tends to shrink the coefficients towards zero but does not set them e...read more

Steps to fit a time series model

• Identify the time series pattern

• Choose a suitable model

• Split data into training and testing sets

• Fit the model to the training data

• Evaluate model performance on testing data

• Refine the model if necessary

• Forecast future values using the model

Outlier values are data points that significantly differ from the rest of the data, potentially affecting the analysis.

• Outliers can be identified using statistical methods like Z-score or IQR.

• Treatment options include removing outliers, transforming the data, or using robust statistical methods.

• Example: In a dataset of salaries, a value much higher or lower than the rest may be considered an outlier.

Ridge and LASSO are regularization techniques used in linear regression to prevent overfitting.

• Ridge adds a penalty term to the sum of squared errors, which shrinks the coefficients towards zero but doesn't set them exactly to zero.

• LASSO adds a penalty term to the absolute value of the coefficients, which can set some of them exactly to zero.

• The geometric interpretation of Ridge is that it adds a constraint to the size of the coefficients, which shrinks them towards the origi...read more

RNN and CNN are neural network architectures used for different types of data.

• RNN is used for sequential data like time series, text, speech, etc.

• CNN is used for grid-like data like images, videos, etc.

• RNN has feedback connections while CNN has convolutional layers.

• RNN can handle variable length input while CNN requires fixed size input.

• Both can be used for classification, regression, and generation tasks.

To reduce model inference latency, optimize model architecture, use efficient algorithms, batch processing, and deploy on high-performance hardware.

• Optimize model architecture by reducing complexity and removing unnecessary layers

• Use efficient algorithms like XGBoost or LightGBM for faster predictions

• Implement batch processing to make predictions in bulk rather than one at a time

• Deploy the model on high-performance hardware like GPUs or TPUs

SQL joins are used to combine rows from two or more tables based on a related column between them.

• INNER JOIN: Returns rows when there is at least one match in both tables.

• LEFT JOIN: Returns all rows from the left table and the matched rows from the right table.

• RIGHT JOIN: Returns all rows from the right table and the matched rows from the left table.

• FULL JOIN: Returns rows when there is a match in one of the tables.

• SELF JOIN: Joins a table with itself.