- Model building
- Model testing
- Applying the model
The three stages to build hypotheses or models in machine learning are:
- Data Preprocessing: This stage involves preparing the raw data for modeling. It includes tasks such as cleaning the data to handle missing values and outliers, transforming variables, and encoding categorical variables into a numerical format suitable for machine learning algorithms. Data preprocessing aims to ensure that the data is in a suitable format for modeling and that irrelevant or noisy information is minimized.
- Model Building: In this stage, various machine learning algorithms are selected and applied to the preprocessed data. Different algorithms may be tested and evaluated to determine which one best fits the data and the problem at hand. This stage also involves tuning the hyperparameters of the chosen algorithms to optimize their performance. Additionally, techniques such as feature selection or dimensionality reduction may be applied to improve model performance or reduce computational complexity.
- Model Evaluation: Once the models have been trained, they need to be evaluated to assess their performance and generalization ability. This involves splitting the data into training and testing sets, or using techniques such as cross-validation to estimate how well the model will perform on unseen data. Various evaluation metrics, such as accuracy, precision, recall, F1-score, or area under the ROC curve, can be used depending on the nature of the problem (classification, regression, etc.). Model evaluation helps in selecting the best-performing model for deployment and provides insights into areas for improvement.
It’s important to note that these stages are iterative, and the process may involve going back and forth between them to refine the hypotheses or models further based on insights gained during evaluation or changes in the data or problem requirements.