Root Mean Square Logarithmic Error or RMSLE

RMSLE, or the Root Mean Square Logarithmic Error, is the ratio (the log) between the actual values in your data and predicted values in the model. Use RMSLE instead of RMSE if an under-prediction is worse than an over-prediction – where underestimating is more problematic than overestimating. For example, is it worse to forecast too much sales revenue or too little?  Use RMSLE when your data has large numbers to predict and you don’t want to penalize large differences between the actual and predicted values (because both of the values are large numbers).

Supervised Learning

Machine-learning algorithms are taught to solve a specific task using training data which contains labels for the “correct answer” for each example

Transfer Learning

This method tries to take training data used for one thing and reused it for a new set of tasks, without having to retrain the system from scratch.

Unsupervised Learning

AI algorithms are given unlabeled data and must make sense of it without any instruction. Such machines “teach themselves” what result to produce. The algorithm looks for structure in the training data, like finding which examples are similar to each other, and groups them in clusters.

Variable Importance

Variable importance is a metric that indicates how much an independent variable contributes to predictions in a model. The higher the value shown for a variable in its ranking, the more important it is to the model generated.

Understanding the significance of predictors provides insights for interpreting results, and also may be useful for improving model quality. For instance, editing data sets to rationalize incorrect or incomplete columns — or removing irrelevant ones — can make models faster and more accurate.

Weak AI

The current state of AI, which does single tasks like playing games recognize images, or predicting outcomes. This is as opposed to Strong AI, also known as Artificial General Intelligence (AGI), which could do anything that humans do.