Holcim Data Scientist Interview Questions, Process, and Tips

XGBoost is a popular machine learning algorithm that uses gradient boosting to improve prediction accuracy.

• XGBoost stands for eXtreme Gradient Boosting.

• It is an ensemble learning method that builds a series of decision trees to make predictions.

• XGBoost uses gradient boosting to minimize errors by adding new models that complement the existing ones.

• It is known for its speed and performance in competitions like Kaggle.

• XG...read more

XGBoost is a popular machine learning algorithm that uses gradient boosting to improve model performance.

• XGBoost stands for eXtreme Gradient Boosting

• It is an ensemble learning method that builds multiple decision trees sequentially

• Each tree corrects errors made by the previous one, leading to a more accurate model

• XGBoost is known for its speed and performance in competitions like Kaggle

• It can handle missing data and is

Clustering is a technique used to group similar data points together based on certain characteristics.

• Clustering involves partitioning a set of data points into groups, or clusters, where points in the same cluster are more similar to each other than to those in other clusters.

• Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN.

• Clustering is an unsupervised learning technique, meaning the ...read more

Hypothesis testing is a statistical method used to make inferences about a population based on sample data.

• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• The null hypothesis assumes that there is no significant difference or relationship, while the alternative hypothesis suggests otherwise.

• Statistical tests are then conducted to determine the likelihood of observing the data if ...read more