Está luchando con un tiempo limitado para el ajuste del modelo. ¿Cómo prioriza la selección de características de manera efectiva?

Time is usually not a major issue when it comes down to LLM tuning using methods like LoRA. The biggest heavy lifting is collecting and preparing a clean, diverse, enough volume data to fine-tune a model for a domain task. Once you have data, the fine-tuning is very easy and fast these days.

Couldn't agree more! Knowing the basics and relevance to the dependent variable is the key. I have a practical experience in this scenario while working on an American Express Project, did the tuning through Random forest classification algorithm on a training set, which took ~20 hours given the huge volume of dataset, but knowing the data and manually eliminating the irrelevant features saves lot of time. A simple binary logistic regression also yielded the same accuracy level of predictions as that of applying the classification algorithm.

Feature selection involves identifying and selecting relevant features for model construction. This step is crucial to avoid overfitting and underfitting. Focus on key methods: filter (using data properties like correlation), wrapper (evaluating feature subsets by training models), and embedded (selection during model training, like Lasso). Understanding these basics helps prioritize feature selection effectively in limited time.

1.Use domain knowledge to identify key features 2. Perform quick correlation analysis to remove redundant features 3. Apply simple models (like decision trees) for initial feature importance ranking 4. Focus on features with clear business impact 5. Assess data quality to ensure reliable features. By focusing on these fundamentals, you can quickly identify the most promising features, allowing for more efficient model tuning within time constraints.

Effective feature selection is crucial for robust ML models. Start by preprocessing data: handle missing values, remove duplicates, and normalize features using Min-Max scaling or Z-score normalization. Assess features with univariate analysis and correlation analysis, using statistical tests like Chi-square and ANOVA. Automated techniques include filter methods (variance threshold, Chi-square test), wrapper methods (RFE, sequential selection), and embedded methods (Lasso, Ridge, Elastic Net). Use dimensionality reduction (PCA, LDA) and model-specific importance (Random Forests, SHAP values). Consider feature clustering and mutual information. Validate with cross-validation and refine with hyperparameter tuning and feature engineering.

In some situations, the algorithm you pick can do much of the heavy lifting for you. For instance, tree-based models inherently are able to account for interactions between features. This is especially useful when the number of features in a dataset is large to begin with; while you can certainly engineer polynomial features and perform feature selection (e.g., Lasso, RFE, etc), the process would be much more resource-intensive than performing feature selection on the original features, then feeding the data into an ensemble model like Random Forest. In the case of tree-based ensemble models, the added benefit is that they can still provide sufficient model explanability via feature importance and SHAP values.

Optimize feature selection, use specialized algorithms. RFE works by iteratively removing the least important features, helping you focus on key predictors. Tree-based algorithms like Random Forest and XGBoost rank features by their importance, giving a clear picture of which ones matter most. Lasso (L1 Regularization) can shrink some coefficients to zero, effectively eliminating irrelevant features. Elastic Net combines L1 and L2 penalties, which is great for handling highly correlated features. Boruta uses shadow features to identify all relevant ones. Sequential Feature Selection adds or removes features based on model performance. Lastly, Mutual Information measures feature-target dependency, highlighting the most informative features.

Leverage Explainable AI (XAI) techniques to interpret and understand the selected features. XAI methods, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), help to elucidate how individual features contribute to model predictions. These techniques provide insights into feature importance and interactions, enabling you to explain the model's behavior to stakeholders clearly.

When using algorithms like RFE, you can run into computational constraints if your model is complex. One way to deal with this is to choose a simpler model for feature selection. For example, if you are using Random Forest, try using fewer and smaller trees. See if there is a configuration that doesn't lower your metrics too much but is small enough to allow RFE to run reasonably fast.

Filter Algorithms: These algorithms evaluate each feature individually and rank them based on their relevance. Examples of these algorithms are Chi-square, Pearson correlation, and information gain. Wrapper Algorithms: These algorithms use a predictive model to score the combination of features. Examples of these algorithms are recursive feature elimination and sequential forward selection. Embedded Algorithms: These algorithms perform feature selection as part of the model training process. Examples of these algorithms are LASSO and Elastic Net.

Perform correlation analysis to identify and remove multicollinear features—those that provide redundant information. This not only simplifies the model but also improves generalization by reducing overfitting. Tools like Pearson correlation coefficient matrices or advanced methods like Partial Least Squares (PLS) can be instrumental.

Effective correlation analysis, start by creating a correlation matrix to display relationships between features and the target variable, using Pearson’s for linear and Spearman’s for monotonic relationships. Visualize this matrix with heatmaps to easily identify strong correlations and redundant features. Employ the Variance Inflation Factor (VIF) to detect multicollinearity, where high VIF values signal problematic redundancy. Examine pairwise correlations to understand direct relationships and minimize overlap. Use feature selection to focus on features highly correlated with the target but not with each other. Finally, apply partial correlation to understand the direct relationship between two variables while controlling for others.

Correlation Matrix: Explore the correlation matrix to identify highly correlated features. Retaining only one representative from a group of highly correlated features can reduce redundancy. Eg: Analyzing customer demographics for a retail store. "Income level" and "household size" might show high correlation. Keeping only one of these features for initial model building can be efficient. Visualization Techniques: Utilize heatmaps or scatter plots to visualize correlations between features and the target variable. Eg: Plotting "customer satisfaction score" against various product features can reveal which features have the strongest positive or negative correlations, guiding initial feature selection.

On Data sciences we can use Peason correlation to identify the correlation between variables. Than we can take action for each that target. Some data doesn't have correlation between each other, so we identify the data that increase/decrease our variable.

When pressed for time in feature selection, leveraging domain knowledge is crucial. Understanding your data's context helps identify the most important features quickly. Consult with experts or research key variables that have proven significant in similar scenarios. For example, in healthcare projects, knowing critical medical indicators for diagnosing conditions can streamline feature selection. This approach speeds up the process and ensures your model is built on relevant data. Although it might not capture every bit of potential accuracy, domain expertise provides a reliable, justifiable foundation, leading to a robust and understandable model, even under tight deadlines.

I would put this step ahead of everything else. Diving straight into « technical matters » can ultimately lead to dangerous errors. I would also (once again) mention the temptation of using a model without paying close attention to its underlying principles and limitations. This can be especially challenging with modern computers capabilities.

Without time for a deep dive into feature selection, there is one thing that is absolutely essential: domain knowledge. Selecting features based on outside knowledge of the application will usually result in fruitful results and it is an easily approach to justify to stakeholders. Although you might not be able to squeeze out that extra 10% in accuracy, you can't go wrong with domain expertise.

We can have all the fancy statistical methods in the world but without domain knowledge to guide our exploration, we cannot make any sense of our data

Engage with individuals who understand the business context and the relationships between variables. Their insights can inform which features are likely to be most influential. Eg: Building a model to predict credit risk. Collaborate with financial analysts to identify key features like debt-to-income ratio and credit score, prioritizing them for initial model development. Prioritize features that are not only statistically significant but also actionable for the business. Eg: A churn prediction model might identify "number of customer support interactions" as a significant feature, but if the business lacks resources to address this, focusing on features related to product usage or pricing might be more impactful.

Struggling with limited time is a highly likely issue that I may particularly face. :) One of the ways I can address this problem is by applying an iterative approach to the feature selection process. This involves initially building a simple baseline model to establish a performance benchmark. I would train the model with just a small number of features and gradually add more based on their importance. I would then iteratively evaluate their impact on the model performance and prioritize the ones that show the most significant improvements. This strategy would help me to manage the model's complexity, while ensuring positive impacts of every additional feature on the results.

Iterative approach for feature selection by starting with a few features. Gradually add features, evaluating their impact on model performance with cross-validation. Techniques include forward selection (adding one feature at a time) and backward elimination (removing one feature at a time). Monitor metrics like accuracy or F1 score to assess the benefit of each feature. Use feature importance scores from models like Random Forest to guide your selection. Ensure added features improve performance without adding unnecessary complexity. This method balances efficiency and effectiveness in feature selection.

In data science, it is very important to study mathematical concepts to present our models easily, so that a quantitative research approach can be greatly promoted and the activities used in daily life can be easily analyzed.

Adopt an iterative approach to feature selection. Start with a broad set of features, refine them through algorithms and correlation analysis, and then loop in domain knowledge to further fine-tune the selection. Iteratively test the impact of adding or removing features on model performance, which leads to a more refined model over time.

Constantly assess your model's performance after each feature selection round. Implement evaluation metrics such as F1 score, precision, recall, and ROC-AUC to gauge your model's predictive power. This iterative approach not only provides immediate feedback on the impact of each feature but also helps identify when adding more features no longer contributes to performance improvement. Leveraging these insights allows you to fine-tune your model efficiently, ensuring optimal feature selection within the constraints of limited time.

Evaluation of model metrics is essential to gauge the model performance. Actions can be taken by considering different model metrics like confusion matrix, cross-validation, AUC-ROC curve, and many more classification metrics help to improve the model accuracy.

Always evaluate Performance of the work. Use methods to check your accuracy for your project. When you like the result that's the project to present. Loop your work step by step to prevent any misleading information on the end.

Finally, rigorously evaluate the performance of your model with each iteration of feature selection. Use metrics appropriate to your problem statement, such as accuracy, precision, recall, F1 score for classification problems, or MSE, RMSE for regression tasks. Performance evaluation is key to understanding the trade-off between model complexity and accuracy, guiding your feature selection process to optimize for the best outcome.