Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve the name of the people whose name starts with a, and the first 3 chars of the company name What is recall vs precision? How to reduce the variance from the ensemble model. How does the splitting in Random Forest take place?

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Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.
To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different subsets of the data and averaging their predictions. Boosting focuses on improving the performance of weak learners by combining them into a strong learner. Stacking involves combining multiple models using another model.
In Random Forest, the splitting of nodes is based on randomly selected features at each node. The algorithm selects the best feature from a random subset of features to split a node, which helps in reducing overfitting and improving generalization.
Example: Recall = TP / (TP + FN), Precision = TP / (TP + FP)
Example: Bagging techniques like Random Forest can help reduce variance in ensemble models.
Example: In Random Forest, each tree is trained on a random subset of features to reduce overfitting.

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