13.3 Model Evaluation
Now that you have gained familiarity with the different types of machine learning and some of the basic algorithms, it is important to delve into the topic of model evaluation. This is a critical area that is equally, if not more, important than the earlier concepts covered. While creating a model may seem fantastic, it is imperative to know whether it is good or not, and this is where model evaluation comes into play.
The process of model evaluation is vital to ensure that the model performs optimally and produces accurate predictions. It involves assessing the performance of the model across different metrics, including precision, recall, accuracy, F1 score, and more. Through model evaluation, you can determine whether the model is overfitting or underfitting, and make necessary adjustments to improve its performance.
Furthermore, model evaluation is not a one-time process; it is an ongoing process that requires constant monitoring and fine-tuning. By doing...