It’s easier to explain through an example, so here it goes:
A probability is returned using logistic regression. You may utilize the returned chance “as is” or convert it to a number that is binary. A logistic regression machine learning model with a result of 0.9898 predicts that an email is extremely likely to be spam. Another message (email) with a score of 0.0002 on the same regression ML model, on the other hand, is almost certainly not spam. But how about an email with a 0.6843 prediction score? A classification threshold value must be defined if you want to transfer a logistic regression value to a binary category. A value greater than that denotes “spam,” whereas a value less than that suggests “not spam.” It’s easy to assume that the classification threshold is always going to be 0.5… however, machine learning thresholds are problem-specific and must be fine-tuned.
The best threshold for the classifier may be derived directly in some circumstances, such as when utilizing Precision-Recall Curves and ROC Curves. In other circumstances, a grid search can be used to fine-tune the threshold and find the best value.
A lot of machine learning methods are able to predict class membership probability or score. This is valuable in general since it offers a measure of a prediction’s confidence or uncertainty. It also gives more detail than just anticipating the interpretable class label.
A couple of classification tasks need a precise forecast of the class label. This implies that even if a class membership probability or score is anticipated, it must be transformed into a precise class name. The threshold governs the choice to turn a projected probability or scores into a class label. For normalized projected probabilities in the range of 0 to 1, the threshold is set to 0.5 by default.
In a binary classification issue with normalized predicted probabilities, class labels 1 and 0, and a threshold of 0.5, for example, values less than the threshold are allocated to class 1 while values greater than or equal to the threshold are allocated to class 0.
Class 1 = Prediction < 0.5
Class 0 = Prediction => 0.5
The default threshold might not be the best way to understand the anticipated probability is the issue here.
This could happen for a variety of reasons, including:
Many strategies, such as resampling the training dataset and designing customized versions of machine learning algorithms, can be utilized to handle an unbalanced classification problem.
Nonetheless, changing the decision threshold may be the easiest way to address a significant class imbalance. This strategy is frequently disregarded by practitioners and research academics alike, despite its simplicity and effectiveness.
On a test dataset, a ROC curve is a diagnostic map that examines a collection of probability predictions produced by a model and is one of the classification metrics in machine learning.
The true positive rate and false positive rate of the predictions on the positive (minority) class are interpreted using a set of different thresholds, and the scores are displayed in a line of rising thresholds to generate a curve.
The Receiver Operating Characteristic curve, or ROC curve, is a figure in which the x-axis represents the false-positive rate, and the real positive rate is represented on the y-axis.
A no-skill classifier’s “curve” (which always predicts the majority class) is shown by a diagonal line on the plot from bottom left to top right, and a point in the top left of the plot represents a model with perfect skill.
The ROC Curve is a helpful diagnostic tool for determining the trade-off between different thresholds, and the ROC AUC is a useful metric for comparing models based on their overall capabilities.
A precision-recall curve, unlike the ROC Curve, focuses solely on the performance of a classifier on the positive (minority class).
The ratio of true positives to the sum of true positives and false positives is known as precision. It indicates how well a model predicts the positive class. The number of true positives divided by the total of true positives and false negatives is used to determine recall. The terms recall and sensitivity are interchangeable.
A precision-recall curve is generated by defining crisp class labels for probability predictions over a range of thresholds and measuring the accuracy and recall for each threshold separately. The thresholds are plotted in increasing order on a line plot with recall on the x-axis and accuracy on the y-axis.