Machine learning, a significant subset of artificial intelligence, has several different methods and techniques that are designed to help machines learn from data and make intelligent decisions. One such approach is supervised learning. This form of machine learning operates under the prerequisite that the output variable, also known as the target variable, is known. There are two subtypes within supervised learning: regression and classification.
Regression in supervised learning takes place when the target variable operates on a continuous scale. An illustrative case would be predicting real estate prices based on factors such as area, number of rooms, availability of a lawn, or presence of a swimming pool.
In contrast, classification involves the use of discrete or categorical target variables. For instance, a medical prediction model might classify whether a person is likely to develop diabetes, utilizing inputs such as blood pressure, glucose levels, and insulin metrics.
The critical distinction between regression and classification lies in the nature of the target variable – continuous for the former and categorical for the latter.
Moving forward, let’s delve into some aspects of model performance evaluation, primarily precision and recall. Precision is a relevant evaluation metric when the aim is to reduce the number of false positives, meaning cases where a negative result is erroneously predicted as positive. This becomes particularly essential when a high cost is associated with these false positives in terms of resources or other factors.
On the other hand, recall should be employed when the model needs to minimize false negatives, where positive cases are inaccurately predicted as negative. This criterion becomes crucial when missing out on positive cases implies a high opportunity cost.
Another concept to grasp in model evaluation is misclassification. Misclassification denotes instances where the model inaccurately predicts an observation’s class. For instance, if a person without diabetes (class 0) is predicted to have diabetes (class 1), the model has misclassified that observation.
Understanding the orientation of the confusion matrix, a performance measurement for machine learning classification, is pivotal to identify True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). It’s not uncommon to find that the confusion matrix appears ‘inverted’ in practice, compared to how it is traditionally taught, which is primarily due to the differing approach of various libraries such as sklearn.
Take the case of diabetes prediction, where class 1 represents ‘diabetic’ and class 0 ‘non-diabetic’. TP would be those instances where the model accurately identifies a person with diabetes, and TN where a non-diabetic person is correctly classified. Conversely, FP represents cases where a non-diabetic person is misclassified as diabetic, and FN denotes instances where a diabetic person is inaccurately predicted as non-diabetic. The alignment of actual and predicted labels on the matrix will help pinpoint these classifications.
Lastly, let’s talk about handling categorical variables. We use label encoding when the values of a variable demonstrate an inherent order, such as rating a product from ‘bad’ to ‘good’ to ‘very good’. However, for categorical values that lack a defined order, like colors (‘red’, ‘blue’, ‘green’), creating dummy variables is the optimal approach.
Selecting the right evaluation metric is conditional on the problem at hand. If the aim is to decrease false negatives, the model should strive to improve recall. Conversely, enhancing precision will help reduce false positives. If both need to be lowered, the F1-Score, which considers both precision and recall, should be increased. For a comprehensive and accurate model prediction, one should focus on elevating the accuracy of the model, effectively enhancing true positives and true negatives.