What are the steps for choosing the best model from a set of candidates?

The first step in choosing the best model from a set of candidates is to clearly define the goal of your model. This includes understanding the specific problem you are trying to solve, the type of predictions you need to make, and the criteria for success. By establishing clear objectives, you can more effectively evaluate each candidate model based on how well it meets these predetermined goals and performs on relevant metrics, ensuring the selection aligns with your project's aims.

Remember there no free lunch theorem especially in Data Science. If you want to bring best precision results you have to tune in the model and first set up baseline fast with simpler technique. Focus on the type of problem you are solving first. Look for standard approaches before bringing perfections. It's simple, if your base is strong with whole context in place it would be way to simple for you in the later stage.

As a data scient, I say you have to Clearly understand the problem you are trying to solve and the desired outcome rhe nest step Collect relevant data and prepare it for training and testing then you should determine the appropriate performance metrics for your problem and apply various machine learning algorithms to your preprocessed data and train the models , also use a validation dataset to assess the performance of each model and fine-tune hyperparameters to improve their accuracy and compare model performances and the end test on unseen data and select the final model and monitor and retrain.

The choice of the best model from a set of candidates should align closely with the overarching business goals and thresholds. What i mean is that your selection process should be driven by the specific outcomes the business aims to achieve and the same time ensure the chosen model aligns to the ethical and legal requirements.

Define Metrics: Establish evaluation metrics like accuracy, precision, recall, or F1 score. Data Split: Segregate data into training, validation, and test sets. Train Models: Train each candidate using suitable algorithms. Validate: Evaluate models on the validation set using defined metrics. Selection: Identify top performers based on validation results. Hyperparameter Tuning: Fine-tune parameters to optimize performance. Test Performance: Assess final models on a separate test set. Comparison: Compare model performance across metrics. Consider Trade-offs: Balance performance with resources and interpretability. Final Selection: Choose the model with the best performance on validation and test sets, meeting project requirements.

Exploratory Data Analysis (EDA) is the secret sauce in ML modeling because it lays the foundation for understanding the intricacies of the dataset. By visualizing and analyzing the data's distribution, correlations, and outliers, EDA helps in identifying key features, understanding their impact on the target variable, and formulating hypotheses. This process is crucial for making informed decisions regarding feature selection, data preprocessing techniques, and model selection. Ultimately, EDA empowers data scientists to create more accurate and robust machine learning models by uncovering valuable insights hidden within the data.

The first step is to crystallize the objective of your modeling endeavor. What business problem are you addressing? Are you predicting, classifying, clustering, or optimizing? A well-defined goal acts as a north star, guiding all subsequent steps in the model selection process.

Data exploration is basically akin to a detective examining clues. Delve into your dataset to understand its characteristics, identify patterns and anomalies, and form hypotheses. This phase lays the groundwork for feature selection and model suitability.

Exploring the data involves gaining a deep understanding of the dataset by examining its characteristics, distributions, and relationships between variables. By visualizing the data through techniques like histograms, scatter plots, and correlation matrices, you can identify patterns, anomalies, and potential challenges that may influence model performance. Exploratory data analysis (EDA) also helps in selecting relevant features, handling missing values, and identifying outliers, which can have a significant impact on the efficacy of different models.

Exploratory Data Analysis (EDA) plays a crucial role in identifying patterns in data science projects. To analyze your data effectively, it's essential to understand it with the help of visual distributions, which can reveal insights into the skewness of the data. Additionally, correlations provide information about the relationships between features. This understanding aids in performing necessary transformations and preprocessing of the data.

Splitting the data is important to assess model performance accurately and prevent overfitting. In the case of unbalanced classes, where one class significantly outnumbers the others, a stratified split ensures proportional representation of each class in both the training and testing datasets. For example, in a medical dataset where only a small percentage of patients have a rare disease, a stratified split ensures that both the training and testing sets contain an appropriate representation of patients with and without the disease. This ensures that the model learns from and is evaluated on a balanced distribution of classes, leading to more reliable performance metrics and better generalization to unseen data.

Once data exploration is complete, the next critical step in selecting the best model is to split the dataset into separate subsets for training, validation, and testing. This process, known as data splitting, ensures that the performance of the selected model is accurately assessed on unseen data. Typically, the dataset is divided into a training set used to train the models, a validation set used to tune hyperparameters and evaluate model performance during development, and a test set used to assess the final model's generalization ability.

The traditional train/valid/test or 80-10-10 split works in most cases, but in my experience, I've only had to use that about 50% of the time. The type of split I had to do mostly depending on the type of problem I was working it, e.g., if it was something time-based, I would prefer a causal split (or) temporal split, where data is split across time so the model does not have access to direct future information. Same goes with recommendation systems, where I would leave entire items/users out of my training set. So in my perspective, there is no one text book way to do a split. It is a function of the problem and what can give you the most realistic estimate of how the model might do in a real-world setting.

Splitting data into training, validation, and test sets. Preprocess the data (e.g., cleaning, scaling) consistently for all models. The validation set is crucial for model selection, while the test set is for final performance evaluation.

The golden rule I follow to select a model is “Minimum cost”. A model with lesser parameters, that runs on lesser resources, is easy to deploy and integrate as API, easy to interpret and understand and finally uses features that can be easily controlled by business is my go to model for deployment. There are exceptions when you really want to achieve very high accuracy for predicting scenarios like frauds, we pick the one thats integrable and offers the best among precision, recall or F1 score depending on the use case.

Ways to compare models- 1.Build Models: Create multiple models using different algorithms. 2.Evaluate Scale: Assess computational requirements for training and deployment. 3.Assess Interpretability: Determine how easily each model's decisions can be understood. 4.Evaluate Ops: Consider ease of deployment and operational efficiency. 5.Analyze Cost: Estimate training, deployment, and maintenance expenses. 6.Compare Performance: Use metrics like accuracy to compare model effectiveness. 7.Select Best Model: Choose the model balancing scale, interpretability, ops, and cost. 8.Iterate and Refine: Fine-tune the selected model and monitor its performance in production.

Compare the performance and characteristics of each model based on your chosen metrics and trade-offs. Consider complexity vs. performance, interpretability, explainability, and resource constraints. You might use statistical tests or visualization techniques to aid comparison.

Comparison of models needs to be focused on some diverse areas like: - Precision, Recall scores - Bias and Variance exhibited by the model - Scalability of the model Now the areas defined above vary on the ML model use-case like regression, classification, PCA, LDA, and many others to choose from. Next important task is to benchmark any version of model as a baseline and iterate upon it. This helps us to clearly understand the segments to expand and iterate on wrt to model tuning and advancements. Post these 2 things are well defined, model comparison and performance boosts become easier to sort out.

Building models is a multi-dimensional iterative process. There are a lot of variables in hand and multiple versions that built to maximize the performance on the decided objective. So, the first main thing to do is to build a setup or use a tool to document as much as these variations in a structured way. Tools like Weights and Biases or MLFlow experiment tracking make life easier or excel sheets are fine too. But the goal is to document three main things - model versioning, what changed compared to the previous version, the hyper-parameters, and performance metrics (depending on the task), and any other notes that you might have. As said many times, start off with something very simple, and use that as a baseline to improve upon.

There are two things I tend to advise: Look at residuals. Correlation coefficients and adjusted r2 are not sufficient. A model that has residual patterns can easily be made better with a different shape. The other thing I always look at is the size of the model. Overfitting is always an issue. Adjusted r2 can alert to this because unlike multiple r2, it can decrease. After this, throw other known data at that same model. Does the model hit? If the model is able to peg data it has no knowledge of for lots of diverse cases, you're probably on the right track.

Model building and iteration is a process that only ends on a deadline that someone gives you. So having intermittent check-ins at a regular basis to go through the set of experiments up until that point, and going through the summary results with the business stakeholders, including your manager, product team, and any other executives involved can help you decide what the next steps are. During these meetings, the focus should be on question 3. in the goal sections about how is success determined aka which metrics are important for this model. For example, if you are building a recommendation system, is the item appearing in the top 10 considered success (Recall) or where the result appears in the top 10 important too (NDCG)?

After training, our model is ready to make predictions. In supervised learning, the new data we use for prediction needs to be similar to the data we trained the model with. This helps the machine make accurate predictions based on what it learned. We need to test our model to see if its predictions are correct or not. We use different methods and metrics like MAE, MSE, RMSE, R2, and confusion metrics to check how well our model is doing. If our predictions are not very accurate, we need to work on improving the model. It’s important to test our model before using it in real-life situations. We should make sure it’s in the right shape and format for deployment.

After you have compared and ranked the candidates, you can select the best model based on your goal and metrics. However, before you deploy or use the model, you need to validate it on the testing data. This is a final check to ensure that your model is not overfitting or underfitting the data, and that it can generalize well to new and unseen data. You should also perform some diagnostic and interpretive analysis, such as checking the residuals, coefficients, or feature importance of your model, to understand how it works and what it captures.

The best approach to validate your model is by testing it on unseen data that was not used during training. If the model demonstrates good accuracy, it can be deployed to a testing environment for further evaluation with real data. Once all checks are satisfactory, the model can proceed to production.

This validation ensures that the selected model generalizes well to unseen data and performs consistently across different datasets. By assessing the model's performance on the test set, data scientists can confirm its effectiveness and reliability in real-world scenarios. Additionally, validation helps in detecting any overfitting issues that might have occurred during model training and selection.

After validating the best model, it's crucial to review its performance comprehensively and refine it if necessary. This step involves analyzing the model's strengths, weaknesses, and areas for improvement based on its performance on the test data subset. you may conduct further experimentation with hyperparameters, feature engineering techniques, or different algorithms to enhance the model's performance. Additionally, gathering feedback from domain experts or stakeholders can provide valuable insights for refining the model to better align with specific business objectives or user requirements.

Reviewing the model and taking action, including retraining if needed, is vital for maintaining performance and relevance: 1. Performance Monitoring: Detect changes in data patterns and respond to performance degradation promptly. 2. Detecting Drift: Identify and address model drift to keep predictions accurate over time. 3. Adapting to Insights: Act on insights gained from reviews to enhance model performance and optimization. 4. Addressing Feedback: Use stakeholder feedback to refine the model and improve user satisfaction. 5. Ensuring Compliance: Update the model to meet regulatory requirements and standards.

To select the best model, it's crucial to fine-tune hyperparameters using techniques like cross-validation to optimize performance. After training, evaluate each model on the validation set using appropriate metrics, such as accuracy or F1-score. Compare these metrics to identify the model with the highest performance. This selected model is then further validated on the test set to ensure its generalization capability and to finalize its selection for deployment. This systematic approach ensures the chosen model is the most effective solution for the given problem, balancing both training and validation performance. Picking the best model is like finding the perfect avocado—sometimes you need to squeeze a few to find the ripest one!

Ne jamais oublier que le meilleur modèle est celui qui est exploitable ! Un modèle très performant mais trop complexe ou trop instable en ré entrainement ne pourra pas être mis en production alors qu'un modèle peut être un peu moins bon sur les métriques mais simple à industrialiser / ré entra?ner sera facile à mettre en place. De plus, certains data scientists passent trop de temps sur de l'optimisation des métriques ce qui a un prix pour les métiers, prix peut être non utile pour les besoins métiers. C'est ce que j'appelle le "syndrome Kaggle".

Other considerations may include computational resources required for training and inference, model interpretability, scalability, and any domain-specific constraints or requirements. Additionally, it's crucial to consider the trade-offs between model complexity and performance, as simpler models might generalize better and be easier to interpret.

The above all focus on identifying a metric for usecase and then finding the best fit model based on performance on that metric in evaluation phase. Besides above, key considerations before finalizing the model are also dependent on implementation aspects like below 1) Ease of implementation 2) Model interpretability for regulation/ compliance or back testing 3) Cost benefit analysis for implementing a complex model to business metrics it delivered.

After defining the goal, conduct a comprehensive literature review. Aim to find 2-3 key papers that present models potentially suitable for your problem. Focus on metrics that directly translate to your business needs. This ensures your chosen model truly addresses your desired outcomes. Instead of using the entire dataset, create a representative sample that captures the full range of potential variations/scenarios within your data. Using this sample, train and evaluate the 2-3 models identified earlier. Choose the model that performs best based on the selected metrics. Finally, use this chosen model to train on the entire dataset for final deployment.

Model selection involves a trade-off between model complexity and performance. Prioritize models that align with the problem's underlying mechanisms and adhere to sound scientific principles. Furthermore, we need to build a train-and-test approach with different models and monitor performance to pick the best one.