How do you evaluate the performance and accuracy of your machine learning models for data analytics?

As the adage states, 80% of data science is in the preparation. A model is only as good as the data provided. That said, the importance of preprocessing cannot be understated. The foundation of any good model is great data quality and great preprocessing.

Do not assume that data far from the mean are "outliers" to be discarded. If you have no evidence that the data collection was flawed, assume the unusual data point is real and keep it.

From my experiences in various industries and in academic research, I found these methods to be in practice - 1) Evaluating the performance and accuracy of machine learning models by use metrics like precision, recall, and the F1 score for classification problems, and mean squared error (MSE) or mean absolute error (MAE) for regression problems 2) Using cross-validation techniques for building further confidence on the model 3) Analyzing confusion matrices can also help understand the model’s performance

To evaluate the performance and accuracy of machine learning models for data analytics, follow these steps: Evaluation Dataset: If you have a dedicated evaluation dataset: Validation Metrics: Choose appropriate metrics based on the model type and problem (e.g., accuracy, precision, recall, F1 score for classification; RMSE, MAE for regression). Cross-Validation: Employ techniques like k-fold cross-validation to assess model robustness and generalization. No Evaluation Dataset: Expert Review: Seek feedback from subject matter experts to interpret model results and ensure they align with domain knowledge. Always ensure that evaluation metrics are aligned with the goals of your analysis and the specifics of your data and model.

Data quality and preprocessing are critical steps before model evaluation, but it’s also important to remember that model performance is not solely dependent on these preparatory phases. While ensuring data completeness, consistency, and accuracy is vital, the choice of evaluation metrics is equally crucial in assessing how well a model generalizes to unseen data. After data preprocessing, you should go beyond basic accuracy metrics and consider factors like overfitting by using cross-validation and other advanced techniques like ROC-AUC for classification or residual analysis for regression models. This holistic approach ensures that both the data and the model are optimally prepared and evaluated.

I think assessing the generalization ability of the models using a previously unseen data set should also be a part of the model selection. So in that sense model evaluation is preceding the selection. In practice there will iteration between the two.

During Model selection and validation in addition to above listed methods it is also imperative to play around with different loss functions and regularization techniques. More often than not different permutations of these can minimize loss and drive model accuracy.

Some additional thoughts that I consider when selecting the right machine learning model are: 1. What metrics are "the right" or most important performance metrics "for the specific use case"? For example with classification models for email or SMS spam prediction, I may be more concerned with minimising False Positives (i.e. don't hide my legit messages in Spam), than False Negatives, i.e. Spam messages wrongly routed to my main inbox. In this case, I would prioritise 'Precision' (TP/(TP +FP)) over 'Recall' (TP/(TP +FN)). In contrast, if I were trying to predict disease infection especially where fatal, 'Recall' (TP/(TP +FN)) is definitely more important as I definitely want to minimize wrong predictions of the infected as uninfected. ...

..cont'd Some additional thoughts that I consider when selecting the right machine learning model are: 2. Compare my models to a 'Baseline' model. This one is not often thought of for people new to data science and ML, but very important. It is often used in Deep Learning. For example, how well did my initial convoluted neural network with one simple hidden layer perform on predicting my 'cats vs dogs' from input images? Record those performance metrics as your 'baseline'. As you continue to work through and iterate through different more complex models, compare performance back to your 'baseline'. If they are worse than the baseline, definitely not worth the extra computational resource or effort & a good way to eliminate 'greedy' models.

..cont'd Some additional thoughts that I consider when selecting the right machine learning model are: 3. Trying different models and methods. E.g. Similar to the previous point on feature engineering and hyperparameter tuning with a keen attention to performance of more complex models vs the simple baseline, it is also key to consider whether for example a Deep Learning model is the best approach vs an XGBoost or Decision Tree algorithm for example. Or in image detection for instance, whether methods such as simple rotation, image flip, resizing and even applying 'filters' like Gaussian Blur can improve image feature detection. This is another key approach to ultimately selecting the "right" machine learning model for your application.

In addition to the quantitative methods of evaluation listed, you should sample your predictions and evaluate them qualitatively to ensure they make sense within the context you're analyzing. Qualitative evaluation can often help you build a stronger intuition about how your model performs when used together with quantitative indicators.

One important aspect is being able to explain how your model is making the decisions it makes. In particular for real world systems, members tend to engage better when they understand why an item is being recommended to them.

“𝙈𝙚𝙩𝙧𝙞𝙘𝙨 + 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙨 = 𝘼𝙘𝙘𝙪𝙧𝙖𝙘𝙮 > 𝙀𝙧𝙧𝙤𝙧𝙨!” 𝟭. 𝗨𝘀𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: Analyzing models using AUC scores and feature importance to ensure reliable predictions. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲: Leveraging SHAP(𝗦𝗛apley 𝗔dditive ex𝗣lanations) values to comprehend influencing factors and refine model performance. These aspects drives robust business models and gives out effective business outcomes.

The best test set is one that did not exist when you finalized calibrating your model, so you can be certain it did not influence the result. If the parameters drift over time, this will show that.

The evaluation process for machine learning models should be tailored to the specific business case at hand. It’s important to identify appropriate metrics that accurately capture how well a model works and what insights can be derived from it. For example, if an organization is using a predictive model for sales forecasting, then metrics such as RMSE (root mean squared error) may be used to measure accuracy. On the other hand, if they are using a clustering algorithm for customer segmentation, then metrics like silhouette scores or Davies-Bouldin Index may be more apt.