SPRINKLR Interview Questions and Answers

To work towards a random forest, you need to gather and preprocess data, select features, train individual decision trees, and combine them into an ensemble.

• Gather and preprocess data from various sources

• Select relevant features for the model

• Train individual decision trees using the data

• Combine the decision trees into an ensemble

• Evaluate the performance of the random forest model

Yes, I have the right tech skills for the Data Scientist role.

• Proficient in programming languages like Python, R, and SQL

• Experience with data visualization tools like Tableau or Power BI

• Knowledge of machine learning algorithms and statistical analysis techniques

• Familiarity with big data technologies like Hadoop and Spark

Bias-variance trade-off is the balance between overfitting and underfitting in a model.

• Bias is the error due to assumptions made in the learning algorithm. Variance is the error due to sensitivity to small fluctuations in the training set.

• High bias leads to underfitting, while high variance leads to overfitting.

• The goal is to find the sweet spot where the model has low bias and low variance, which results in good generalization performance.

• Regularization techniques like Lasso...read more

LLMs can be finetuned by adjusting hyperparameters, training on specific datasets, and using techniques like transfer learning.

• Adjust hyperparameters such as learning rate, batch size, and number of layers to improve performance.

• Train the LLM on domain-specific datasets to improve its understanding of specialized language.

• Utilize transfer learning by starting with a pre-trained LLM model and fine-tuning it on a smaller dataset for specific tasks.

• Regularly evaluate the model's...read more

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 coefficients towards zero

• Both L1 and L2 regularization help...read more

Decision Trees are a popular machine learning algorithm used for classification and regression tasks.

• Decision Trees are a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• They are easy to interpret and visualize, making them popular for exploratory data analysis.

• Decision Trees can handle both numerical and categorical data.

• They can be prone to overfitting, which ca...read more

Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.

• Xgboost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosted decision trees.

• It is widely used in Kaggle competitions and other machine learning tasks due to its efficiency and accuracy.

• Xgboost is known for its ability to handle missing data, regularization techniques, and parallel processing capabilities.

• It can be used for classificati...read more