Why the evaluation is 80% for training and 20% for evaluating but not the opposite?
The allocation of 80% weightage to training and 20% weightage to evaluating in the context of machine learning is a strategic decision based on several factors. This distribution aims to strike a balance between optimizing the learning process and ensuring accurate evaluation of the model's performance. In this response, we will consider the reasons behind this choice and explore the didactic value it offers.
To comprehend the rationale behind the 80% training and 20% evaluation split, it is important to understand the seven steps of machine learning. These steps, which include data collection, data preparation, model training, model evaluation, model tuning, model deployment, and model monitoring, form a comprehensive framework for building machine learning models.
The initial step, data collection, involves gathering relevant data to train the model. This data is then preprocessed and prepared in the data preparation phase. Once the data is ready, the model training phase begins, where the model is exposed to the training dataset to learn patterns and relationships. The model's performance is then evaluated using a separate dataset in the model evaluation phase.
The decision to allocate 80% weightage to training and 20% weightage to evaluation stems from the fact that training is the primary phase where the model learns from the data. During training, the model adjusts its internal parameters to minimize the difference between its predicted outputs and the actual outputs in the training dataset. This process involves iteratively updating the model's parameters using optimization algorithms such as gradient descent.
By assigning a higher weightage to training, we prioritize the model's ability to learn from the data and capture complex patterns. The training phase is where the model acquires its knowledge and generalizes from the training dataset to make predictions on unseen data. The more training data the model is exposed to, the better it can learn and generalize. Therefore, dedicating a significant portion of the evaluation process to training ensures that the model has sufficient exposure to the training data for effective learning.
On the other hand, the evaluation phase plays a important role in assessing the model's performance on unseen data. The evaluation dataset, which is separate from the training dataset, serves as a proxy for real-world scenarios. It allows us to gauge how well the model can generalize its learning to new and unseen instances. Evaluating the model's performance is essential to measure its accuracy, precision, recall, or any other relevant metrics, depending on the specific problem domain.
The 20% weightage given to evaluation ensures that the model is rigorously tested on unseen data and provides a realistic assessment of its capabilities. This evaluation phase helps uncover any potential issues like overfitting, underfitting, or bias in the model's predictions. It also enables the fine-tuning of hyperparameters and model architecture to improve performance.
To illustrate this concept, let's consider a practical example. Suppose we are training a machine learning model to classify images of cats and dogs. During the training phase, the model learns to differentiate between the features of cats and dogs by analyzing a large dataset of labeled images. The more images the model can train on, the better it becomes at distinguishing between the two classes.
Once the training is complete, the model is evaluated using a separate dataset that contains images it has never seen before. This evaluation phase tests the model's ability to generalize its learning and accurately classify new, unseen images. By allocating 20% weightage to evaluation, we ensure that the model's performance is thoroughly assessed on unseen data, providing a reliable measure of its effectiveness.
The distribution of 80% weightage to training and 20% weightage to evaluation in machine learning is a strategic choice aimed at optimizing the learning process while ensuring accurate assessment of the model's performance. By dedicating a significant portion of the evaluation process to training, we prioritize the model's ability to learn from the data and capture complex patterns. Simultaneously, the evaluation phase rigorously tests the model on unseen data, providing a realistic assessment of its capabilities.
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