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Understanding Machine Learning Thresholds through Practical Examples
Let's explore this topic with a relatable example: a logistic regression in machine learning can return a probability. You can use this returned probability directly or convert it into a binary number. For instance, a logistic regression machine learning model indicating a 0.9898 result suggests a high probability of an email being spam. Conversely, an email showing a 0.0002 score on the regression ML model is almost definitely not a spam email. But what if an email shows a 0.6843 prediction score?
To convert a logistic regression value into a binary category, we must establish a classification threshold value. Any value above this threshold indicates "spam," while a value below signifies "not spam." Although it might be tempting to consider the classification threshold as always 0.5, it's crucial to recognize that machine learning thresholds should be customized depending on the problem.
In some cases, the optimal threshold for the classifier can be defined directly using Precision-Recall Curves and ROC Curves. Alternatively, a grid search can help fine-tune the threshold and identify the best value.
Many machine learning methods can predict the probability or score of a class membership. This ability is very useful because it provides a confidence level or uncertainty measure of a prediction. It offers more information than merely predicting an interpretable class label.
Some classification tasks demand an accurate forecast of the class label. This means that even if a class membership probability or score is predicted, it needs to be translated into an exact class name. The threshold governs the decision to convert a predicted probability or score into a class label. By default, for normalized predicted probabilities within the 0 to 1 range, the threshold is set at 0.5.
In a standard binary classification problem with normalized predicted probabilities, class labels of 1 and 0, and a 0.5 threshold, values below the threshold are assigned to class 1, while those above or equal to the threshold are assigned to class 0:
- Class 1= Prediction < 0.5
- Class 0 = Prediction >= 0.5
The challenge is that the default threshold may not always provide the optimal understanding of the anticipated probability. This discrepancy can occur due to various reasons, such as uncalibrated anticipated probabilities, differences between the model's training metric and the metric used for evaluation post-training, significantly skewed class distribution, or varying costs for different types of misclassification.
Various strategies can help address unbalanced classification issues, like resampling the training dataset and designing custom machine learning algorithms. However, adjusting the decision threshold might be the simplest method to respond to substantial class imbalance. Although this technique is simple and effective, it is often overlooked by practitioners and research scholars.
Through tools like ROC-Curve Threshold and Precision-Recall Curve Threshold, machine learning thresholds help predict the 'goodness' of a particular solution. They examine the model's predicted probability set and display the scores on a line of rising thresholds to generate a curve. These curves provide insight into the performance of a classifier and are valuable metrics for comparing models based on their holistic capabilities. They represent the trade-off between different thresholds and help fine-tune the classification threshold for better outcomes.
