What are common mistakes when training ML models and how do you avoid them?

Three suggestions: 1. Choose the right definition of your dependent variable. At times, the way it is defined can lead to data leakage, leading to incorrect results and predictions. 2. Avoid garbage variables. Usually, have some business/statistical hypothesis for inclusion of variables in the model. Analysts tend to add features like 'Customer ID' also to a model, which has no influence whatsoever on the outcome. But if it is a numeric variable, it throw up some random pattern w.r.t. the dependent variable and suggest that 'Customer ID' is an important predictor. 3. Choose the right algorithm, and know about the biases & limitations of the algorithms. In a set of comparable predictive models , the simplest possible model should be chosen.

Beyond controlled tests, consider how algorithms handle real-world complexities: larger, diverse, and messy data, increased user traffic with potentially higher processing demands, and compatibility needs with existing systems and infrastructure. Assess resource requirements (memory, storage, processing power) during selection. Evaluate scalability for growing data and real-time needs, exploring distributed computing or model compression. In production, a slightly less accurate model might be preferable if it significantly reduces cost or latency, considering business impact and user experience.

Common mistakes in ML training: insufficient data, biased data, poor feature selection, improper hyperparameter tuning, data leakage, inappropriate evaluation metrics, model complexity issues, overfitting/underfitting, imbalanced data, and flawed validation strategies. Avoid by understanding data, selecting suitable algorithms, proper validation, and continuous monitoring.

Selecting the right ML algorithm is crucial. It requires understanding both your data and the specific problem you're solving. Begin with a clear analysis of your dataset's characteristics and the problem type (e.g., classification, regression). Utilise exploratory data analysis (EDA) to uncover insights and guide your algorithm choice. Start with simpler algorithms to establish a performance baseline and incrementally test more complex models. In addition, use cross-validation to evaluate each algorithm's performance on metrics such as accuracy and precision. The goal is to match the algorithm's capabilities with your data's nature and your problem's requirements, ensuring a balanced approach that avoids overfitting or underfitting.

Not enough hyper parameter tuning a common mistake. It is really important to do a grid search to find the right learning rate. Before trying a more complex model, ensure that you've sufficiently tried all hyperparams

Neglecting feature engineering can result in models that are either too complex or miss critical information. Feature engineering and selection can highlight key data aspects, boosting performance. Use EDA to identify the most predictive features in your dataset. Apply selection techniques to reduce dimensionality and improve model interpretability and performance. Experiment with feature engineering to create new features to capture crucial information in a way that benefits your models.

Common mistakes in ML model training include using incorrect data. This happens when data is irrelevant, outdated, biased, or noisy. To avoid this, ensure data is carefully collected, cleaned, and preprocessed. Perform exploratory data analysis (EDA) to understand its characteristics, patterns, and relationships. Employ feature engineering and selection techniques to enhance data quality. This prevents inaccurate predictions and ethical issues.

From my experience, dataset engineering is an essential step to be performed prior to any ML/DL model. It is highly recommended to analyze the data features, correlation, and distribution in order to select the most relevant features which result in optimizing the model performance.

Poor quality data translates to poorly performing models. Ensure the data is relevant to the problem, sufficiently large, free of major errors, and representative of real-world scenarios. Pre-processing tasks like cleaning, normalization, and feature engineering are crucial before feeding data into the model.

Carefully select and collect data that is relevant, representative, and sufficient for your problem. Clean and preprocess your data to remove noise, correct errors, and mitigate biases. Perform exploratory data analysis to gain insights into the characteristics, patterns, and relationships in your data. This can help you identify potential issues and guide your data preprocessing efforts. Apply feature engineering techniques to create new features or transform existing ones to improve the quality and usefulness of your data.

Another common mistake is using information that wouldn't be available at prediction time. This leads to unrealistic model performance and causes the model to fail in practice. Implement time-based splits for your datasets, especially in time-series forecasting, to ensure the training data only includes information available up to the point of prediction. This might involve chronological splits where the training set precedes the test set in time, preventing overfitting and ensuring better generalization to unseen data.

Data leakage can severely undermine the reliability of machine learning models. It happens when information from the test set inadvertently influences the training process, leading to inflated performance metrics. This can occur due to various reasons like using the same data for training and testing, including future data in training, or improper separation of target variables. To prevent data leakage, always split data into distinct training, validation, and test sets. Additionally, avoid using features that correlate too closely with the target variable or those unavailable during prediction. This ensures the model learns genuine patterns and performs accurately on unseen data.

In addition to implementing time-based splits for temporal data, feature engineering plays a crucial role in preventing data leakage. It's essential to carefully select features that are available at the time of prediction and avoid incorporating information that would not be accessible in real-world scenarios. Moreover, adopting robust validation techniques, such as cross-validation or holdout validation, helps assess model performance accurately while guarding against leakage. By prioritizing feature selection and validation strategies that align with the temporal nature of the data, we can build more reliable and generalizable machine learning models.

Data leakage occurs when information from the test set leaks into the training set, inflating performance metrics. Maintain strict separation between training and test sets. If performing cross-validation, ensure data splits are done carefully to prevent leakage between folds.

Ignoring data leakage in ML models can lead to an overly optimistic evaluation of their performance. Leakage occurs when test data information influences training, giving the model an unfair advantage. This can result from mixing training and test data, using future data for training, or not properly separating the target variable from features. To prevent this, it's crucial to split data into dedicated training, validation, and test sets, and avoid features correlated with the target variable or unavailable at prediction time, thus ensuring accurate and realistic model evaluations.

The right features can make or break a model's ability to learn effectively. Poor feature selection can lead to overfitting or underfitting. Spend adequate time on feature engineering, including creating interaction terms, polynomial features, and considering domain-specific transformations. Use feature importance to identify which features contribute most to the model's predictive power and focus on those. Investing time in understanding your data and its features can improve model performance. This effort often pays off in the long run.

Common mistakes in ML model training include selecting the wrong algorithm, using incorrect data, ignoring data leakage, overfitting or underfitting the model and tuning wrong parameters. To mitigate these, conduct algorithm selection based on the problem domain and data characteristics, ensure data quality and integrity through preprocessing and validation techniques, implement cross-validation to detect overfitting, and perform hyperparameter tuning systematically. Neglecting model deployment, maintenance, and upgrades can also hinder performance. If on AWS; leverage Sage Maker for end-to-end ML lifecycle management, to ensure secure, scalable model deployment, monitoring, and automated updates, to mitigate the above challenges.

Overfitting happens when the model learns too much from training data, becoming too complex and performing poorly on new data. Underfitting occurs when the model is too simplistic, failing to capture patterns effectively. To avoid these, regularly evaluate model performance using tools like learning curves or cross-validation scores. Apply regularization techniques like L1/L2 regularization to reduce complexity, or increase data, features, or model complexity. Utilize libraries like Scikit-learn for implementing these techniques effectively

Overfitting is the most common problem accounted. It occurs when a model learns to perform well on the training data but fails to generalise on unseen data. To avoid overfitting, we can use techniques such as cross-validation, regularization (L1 or L2), early stopping. Additionally, using more data or simplifying the model architecture can help mitigate overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data. To address underfitting, we can try using more complex models, adding more features to the dataset, or reducing regularization.

To avoid overfitting or underfitting, it's crucial to carefully monitor and evaluate the performance of your models on both training and test data. By using appropriate evaluation metrics and techniques, applying regularization methods, and adjusting model complexity as needed, you can strike the right balance between bias and variance, ultimately improving the generalization ability of your machine learning models. Always keep an eye on the metrices during the model training.

Tuning too many parameters simultaneously without understanding their interactions should be avoided. Each parameter can affect the model differently, and interactions between parameters can complicate the tuning process. Approach parameter tuning methodically, altering one or two parameters at a time to understand their individual and combined effects on model performance. Use techniques like factorial experiments or response surface methodologies to explore the parameter space and identify promising configurations.

One common mistake in training ML models is tuning the wrong parameters. These parameters, like learning rate or tree depth, control model behavior. Tuning them optimally boosts performance, but selecting wrong ones wastes time and can cause suboptimal results or overfitting. To avoid this, understand each parameter's impact, and use systematic approaches like grid search or Bayesian optimization. Employ validation sets or cross-validation for evaluation. Tools like scikit-learn offer helpful functions for parameter tuning.

Focusing on fine-tuning unimportant hyperparameters while neglecting crucial ones hinders performance. Understand the impact of key hyperparameters (e.g., learning rate, number of layers in a neural network). Use techniques like grid search or randomized search for efficient hyperparameter exploration.

While tuning parameters like learning rate or number of neurons is essential, focusing on irrelevant ones can be detrimental. We employed a systematic approach, prioritizing impactful parameters for our mine detection models. This involved understanding each parameter's influence and leveraging techniques like grid search for efficient exploration. By carefully selecting and tuning parameters, we ensured optimal performance without overfitting or underfitting, crucial for real-world deployments.

After model deployment, several challenges often emerge that can impact the performance and efficacy of the deployed machine learning system. One significant challenge involves monitoring and managing model drift, where the underlying data distribution evolves over time. Additionally, ensuring scalability and robustness in production environments is crucial, as unexpected increases in workload or changes in user behavior can strain deployed models and infrastructure. Moreover, maintaining model interpretability and compliance with regulatory requirements presents ongoing challenges. Furthermore, addressing security concerns, such as safeguarding against adversarial attacks or unauthorized access to sensitive data, remains paramount.

Building a successful machine learning model doesn't end with training; it requires effective deployment and ongoing maintenance. Neglecting these aspects can render even the best models ineffective. Prioritize developing robust deployment pipelines and monitoring systems to ensure seamless integration into production environments. Establish procedures for model retraining and updates as new data becomes available or the underlying problem evolves. Additionally, consider factors like scalability, reliability, and interpretability when deploying and maintaining models in real-world settings.

Building an ML model is just the start! Don't neglect deployment & maintenance: - Plan: Choose a platform (cloud, containers) and design user access (app, API). - Monitor: Track metrics (accuracy, efficiency), set up alerts for issues. - Gather feedback: Ask users for input, analyze model metrics for insights. - Update: Retrain with fresh data, refine parameters, continuously iterate for optimal performance. By following these steps, you can ensure your ML model is effectively deployed, maintained, and continues to deliver value over time

Machine learning is not a one-and-done process. Plan how the model will be deployed, served in production, and monitored for performance degradation over time. Develop strategies for retraining and updating models as new data becomes available or conditions in the real world change.

My work on the AMMO program emphasized the importance of continuous monitoring and maintenance for deployed ML models. We understood that simply deploying our mine detection models wasn't enough. We established processes to track performance metrics and user feedback, allowing us to identify and address potential issues. This proactive approach ensured our models remained reliable and accurate in the real world, highlighting the crucial role of maintenance in the ML lifecycle beyond initial development

Common mistakes when training ML models include overfitting, underfitting, inadequate data preprocessing, and ignoring model evaluation. To avoid them, use techniques like cross-validation, regularization, feature scaling, and careful selection of evaluation metrics.

The successful training of a ML model involves several crucial steps. It begins with choosing the right algorithm and using relevant, clean data. During the training phase, it's important to avoid data leakage, overfitting or underfitting, and to focus on tuning the parameters that significantly impact the model's performance. Once the model is deployed, continuous monitoring and maintenance are necessary as data patterns can change over time. Therefore, all these aspects are equally important and contribute to the effectiveness of a machine learning model.

Common mistakes when training ML models: 1. Insufficient data: Ensure you have enough high-quality data for training. 2. Overfitting: Regularize the model and use techniques like cross-validation. 3. Underfitting: Increase model complexity or gather more relevant features. 4. Data leakage: Carefully separate training, validation, and test datasets. 5. Ignoring evaluation metrics: Choose appropriate metrics that align with your problem. To avoid these mistakes, validate data quality, apply proper regularization, tune model complexity, adhere to data splitting protocols, and prioritize relevant evaluation metrics.

points 1 to 6 are all valid, provided you are using the right model. Look in huggingface, nemo or other repos before reinventing the wheel. And keep train and test data separate.