Rapid Fire on Artificial Intelligence

Whenever data is missing or corrupted, you either replace it with another value or drop those rows and columns altogether. In Pandas, both isNull() and dropNA() are handy tools to find missing or corrupted data and drop those values. You can also use the fillna() method to fill the invalid values in a placeholder—for example, … Read more

You have to first split the data set into training and test sets. You also have the option of using a cross-validation technique to further segment the data set into a composite of training and test sets within the data. Then you have to implement a choice selection of the performance metrics like the following: … Read more

Inductive learning describes smart algorithms that learn from a set of instances to draw conclusions. In statistical ML, k-nearest neighbor and support vector machine are good examples of inductive learning. There are three literals in (top-down) inductive learning: Arithmetic literals Equality and inequality Predicates In deductive learning, the smart algorithms draw conclusions by following a … Read more

When you have underfitting or overfitting issues in a statistical model, you can use the regularization technique to resolve it. Regularization techniques like LASSO help penalize some model parameters if they are likely to lead to overfitting. If the interviewer follows up with a question about other methods that can be used to avoid overfitting, … Read more

You should update an algorithm when the underlying data source has been changed or whenever there’s a case of non-stationarity. The algorithm should also be updated when you want the model to evolve as data streams through the infrastructure. The correct answer to the question “When is it necessary to update an algorithm?” would be: … Read more