How do you compare Machine Learning models visually?

A confusion matrix offers a quick and detailed evaluation of your model's performance. It comprises four key metrics: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). Utilizing these values, you can calculate critical metrics like Precision (TP / (TP + FP)), Recall (TP / (TP + FN)), Accuracy ((TP + TN) / (TP + TN + FP + FN)), F1 Score (2 * (Precision * Recall) / (Precision + Recall)), True Positive Rate (TPR, same as Recall), and False Positive Rate (FPR, FP / (FP + TN)). Each of these metrics provides valuable insights into different aspects of your model's performance, allowing for a balanced assessment based on your specific requirements.

Advice from experience: It is often beneficial to convert the raw counts in a confusion matrix to percentages to facilitate a nuanced interpretation. This serves dual purposes: first, it allows for the mapping of model performance to the underlying class distributions; second, it enables straightforward comparisons across different models or datasets. However, it is crucial to approach this percentage-based interpretation with caution. In isolation, percentages can be misleading and must be assessed with absolute class counts to provide a fully informed evaluation.

Practical POV: Club decision boundary plot with confusion matrix. The decision boundary visually illustrates how the model separates classes, complementing the confusion matrix. 1. Plot the decision boundary on the feature space used to construct the confusion matrix. 2. Generate one confusion matrix per class, and overlay the decision boundary for that class on the same plot. This associates performance metrics directly with the decision boundary. 3. For multi-class problems, plot a one-vs-all decision boundary for the class with the highest confusion to inspect why it's challenging to classify. Use 1. sklearn.inspection.DecisionBoundaryDisplay - maps feature space to 2D 2. sklearn.inspection.plot_boundary - is more customizable

Confusion matrices display the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) for each categorical class, arranging these counts in a table format. The table provides a detailed evaluation of a model's performance, offering insights into metrics like accuracy, precision, and recall. In my perspective, the confusion matrix provides a more nuanced view of how well the model performs across different classes, unlike single-metric scores such as accuracy.

Few methods for comparing machine learning models visually: ROC Curve: A model's effectiveness for binary classification problems is depicted graphically by the Receiver Operating Characteristic (ROC) curve. At different threshold values, it illustrates the trade-off between genuine positive rate (sensitivity) and false positive rate (1-specificity). When comparing ROC curves of different models, one with a curve that is closer to the top-left corner typically performs better. Precision-Recall Curve: Precision-recall curve provides more information than ROC curve for datasets that are unbalanced. At various probability thresholds, it graphs precision versus recall. In general, better models have curves that are closer to top-right corner.

Classification metrics like Accuracy and Precision are computed on prediction outcomes for which a threshold is set that discretizes probabilities into class labels. ROC Curve evaluates the model against all thresholds in [0, 1], hence is unbiased towards thresholds unlike above metrics. To compare models with a metric like Accuracy through ROC Curve, AUC is computed. If AUC <= 0.5 for binary classification, say 0.3, we can interchange GT labels. This flips ROC Curve along y = x and the new AUC will be 1 - AUC i.e., 0.7. This implies that we can always build a model that performs better than random generator (AUC = 0.5). To check for overfitting: 1. Train ROC and Val ROC curves should be similar 2. Train AUC should be similar to Val AUC

The Receiver Operating Characteristic (ROC) curve is a graphical representation of a binary classification model's performance across different threshold settings. It plots the True Positive Rate (Sensitivity) against the False Positive Rate (1 - Specificity) at various thresholds. The area under the ROC curve (AUC-ROC) quantifies the model's ability to distinguish between classes; a higher AUC indicates better performance. ROC analysis helps in selecting optimal thresholds and comparing different models. It's a valuable tool for evaluating classifiers and making informed decisions, especially in medical diagnosis, fraud detection, and more.

The AUC measures the overall performance of a binary classification model across all possible thresholds. It quantifies the model's ability to discriminate between positive and negative classes. A higher AUC indicates better discrimination, with a perfect classifier having an AUC of 1.

I would emphasize that the ROC curve empowers you to make informed decisions. It's a compass guiding you through the model's ability to distinguish between classes, aiding in model selection and threshold optimization. With Scikit-learn's roc_curve and roc_auc_score functions, you can compute these crucial metrics. Then, with Matplotlib's plot, visualize and compare multiple models on a single graph. The ROC curve is your ally in navigating the intricacies of model performance.

The learning curve of a model lets us in the idea of how well the model is learning from the provided data. By carefully examining these curves one can understand the model complexity and whether or not the model has enough learnable parameters to converge. It's also worthwhile to point out that the learning curve over the validation set would tell us how well the model reacts to the unseen data, this could help determine if the model is under fitting or over fitting. A trembling learning curve is also indicative of issues with the data and so is a sudden spike in the curve. Hence the Learning curve is a good way to visualise and compare how different models behave and it also shines light upon the quality of data used.

A learning curve visualizes the relationship between a model's performance and the amount of training data used. It shows how accuracy or error changes as training data increases. Initially, as data grows, models improve; however, there's a point where additional data provides minimal gain. Learning curves reveal overfitting (gap between training and validation curves) or underfitting (both curves converge at low performance). Analyzing learning curves guides data collection, helps tune model complexity, and aids decision-making about gathering more data or refining algorithms for optimal performance.

Learning curves are a powerful tool for diagnosing overfitting and underfitting, and they also provide insights into how model performance changes with varying amounts of training data. By analyzing these curves, you can make informed decisions about model complexity, data sufficiency, and optimization strategies to improve a model's ability to generalize to new data.

Aside from explaining what a learning curve is. I find it very helpful to tweak or optimize the ML model, dataset and hyperparameters. It shows how much training or verification datasets can affect the model fitting. It is one great tool to assess the model and finetune the dataset or the hyperparameters

I would stress that the learning curve is your diagnostic tool for model performance. It unravels the mysteries of overfitting, underfitting, or achieving the coveted fit. With Scikit-learn's learning_curve function, you can craft these illuminating curves for diverse models. Then, with Matplotlib's plot, bring them together in a visual narrative. The learning curve equips you with the insights to optimize your model's performance and determine the optimal data size for refinement.

Feature importance gauges the contribution of individual features in a predictive model's output. It identifies which attributes have the most impact on predictions. Common methods include tree-based techniques like Gini impurity and permutation importance. Higher values signify greater influence. Feature importance aids in feature selection, understanding model behavior, and uncovering insights. It's critical for dimensionality reduction, improving model interpretability, and optimizing algorithms. In fields like finance, healthcare, and image recognition, discerning crucial features enhances model accuracy and decision-making.

Feature importance refers to the process of evaluating and ranking the input variables (features) used in a machine learning model to understand their contribution to the model's predictive performance. It helps identify which features have the most significant influence on the model's predictions. Various techniques, such as feature importance scores and feature selection methods, can be employed to assess the relevance of individual features. Understanding feature importance aids in model interpretation, dimensionality reduction, and improving model generalization by focusing on the most informative features while discarding irrelevant or redundant ones.

I underscore the transformative power of feature importance analysis. It enables the pruning of irrelevant or redundant features, streamlining model complexity. In Scikit-learn, models like decision trees and random forests offer the feature_importances_ attribute, providing these scores. Harness Seaborn's barplot function to craft visually engaging horizontal bar charts, making feature importance insights easily digestible. Embrace this tool to enhance model understanding and optimize performance.

A primary purpose of creating a residual plot is to assess how well the regression model fits the data. In a well-fitted model, the residuals should exhibit a random scatter around zero, indicating that the model is capturing the variance in the data adequately. Residual plots also help in verifying the assumptions of linear regression. These assumptions include linearity, independence of errors, homoscedasticity (constant variance of residuals), and normality of residuals. By examining the plot, you can assess whether these assumptions hold.

A residual plot visually assesses the performance of regression models. It plots the residuals (the differences between actual and predicted values) against the independent variable(s). Patterns in the plot reveal whether the model captures the underlying relationships. Ideally, residuals should be randomly scattered around zero, indicating a well-fitted model. Patterns like curves or clusters signify model inadequacies like non-linearity or heteroscedasticity. By examining residual plots, practitioners diagnose model flaws, refine assumptions, and make necessary adjustments for accurate predictions in fields like economics, engineering, and social sciences.

cluster plots are essential tools for unsupervised learning tasks, helping data analysts and researchers explore data structure, evaluate clustering performance, and make informed decisions about the number of clusters and their interpretation. They play a crucial role in understanding patterns and relationships within complex datasets.

A cluster plot illustrates the grouping or clustering of data points based on their similarity. It visualizes how instances are clustered together in multi-dimensional space. Various clustering algorithms like k-means or hierarchical clustering form the basis for such plots. Data points within a cluster are closer to each other than to points in other clusters. The plot's structure reveals insights about natural groupings, patterns, or anomalies in the data. Cluster plots aid in exploratory data analysis, customer segmentation, image recognition, and more, helping researchers and analysts identify hidden relationships and structures within datasets.

What is better than a 3D visualization of all the data points as clusters. I find it very helpful to understand how the model algorithm and parameters output

Knowing all of this is important, but do not be afraid to get your hands dirty with practice. Apply any knowledge you acquire as quickly as possible to your job or pet project. Use this knowledge in Kaggle competitions, or write a blog post about what you've learned. This way, you will definitely remember it.

While the article is good, it seems "overfitted" to problems that can be solved by a tabular data. Here is a non-comprehensive list of things to consider if you have deep neural networks. 1. Embedding projections - Project high-dimensional embeddings down to 2D for visualization. 2. Saliency Maps - Heatmaps showing which parts of the input most affected a model's output. Checks for focus. Use methods like Grad-CAM, SmoothGrad, etc. 3. Counterfactual visualizations - Demonstrate the minimal changes to input needed to alter predictions. 4. Adversarial examples - Visually show inputs that cause models to fail in order to diagnose robustness. 5. Many others like Activation maps, Filter visualizations, gradient flow maps

Comparing and visually assessing the key performance indicators produced by each model’s confusion matrix is what practitioners do in most cases. The most important measure is the F1 Score, which is the harmonic mean of recall and precision. Top models produce high F1 scores, indicating that both Precision and Recall are high. Medium F1 scores indicate that either Precision or Recall is high while the other is low. Low F1 scores indicate that both Precision and Recall are low.

Learning Curves: Show how models learn over epochs to spot issues like overfitting ROC and Precision-Recall Curves: Assess classification models by examining trade-offs. Confusion Matrix: Break down classification performance into true/false positives/negatives Feature Importance: Visualize which features influence predictions. Decision Boundaries: See how models separate classes in 2D/3D space. Model Comparison: Compare models using plots or tables of metrics. Ensemble Methods: Visualize how ensemble methods improve predictions. 8Dimensionality Reduction: Reduce high-dimensional data for easier visualization. Cross-Validation Results: Assess model stability and consistency across data splits.

A scatter plot of ground truth and model prediction quickly informs the reader about the model's performance, where the model is making errors, and by how much. Each point on the plot represents a sample from a testing set: the x component of the point is the corresponding ground truth value, while the y component is the model's prediction. An ideal model perfectly predicts the ground truth, so its scatter plot is the diagonal line (y = x), often shown in these scatter plots for comparison. It is also important so set the length of both x and y axis to be equal, for an aspect ratio of 1 : 1; it makes for an easier comparison.