F-test is a test of the significance of the overall model. In other words, a significant F-test indicates that our regression model fits the data better than the model without any predictors.

F1-score is the harmonic mean of the sensitivity (recall) and precision (positive predicted value); a high F1 score means low false positives (FP) and low false negatives (FN).

See Principal component analysis

FN is the prediction that wrongly indicates the absence of the event.

FP is the prediction that wrongly indicates the presence of the event.

See Variable importance

See Predictors

Forward propagation is the process of transmitting input signals through the network, layer by layer, to produce an output. Each neuron in a layer receives input from the previous layer, performs computations using its activation function, and passes the output to the next layer.

Forward stepwise is the method in which the model starts with no predictor variables and iteratively adds one variable at a time, and at each step, the variable that improves the model’s performance the most is added to the model until no further improvement is achieved or a predefined stopping criterion is met.

Frequentist statistics, also known as frequentist inference, is a statistical framework and approach to data analysis that focuses on the concept of repeated sampling and long-run frequencies of events.