What are the best visualizations for showing machine learning model accuracy?

A Confusion matrix is a NxN matrix that compares actual target values to model predictions, where N is the total number of target classes. It will display the number of true positives, false positives, true negatives, and false negatives for binary classification models, and a snapshot of the model's precision, recall, and f1-score. True Positive/True Negative- Actual value was positive/negative, and the model predicted a positive/negative value. False Positive/Negative – Actual value was negative/positive, but the model predicted a positive/negative value. With scikit-learn, seaborn its easy to create matrix and enhance readability by assigning colors, adding annotations, and labels.

A confusion matrix is a table used in binary classification models to display the counts of true positives, false positives, true negatives, and false negatives. It is a valuable tool for assessing a model's precision, recall, and f1-score, while also highlighting common errors and biases. You can easily create a confusion matrix using libraries like scikit-learn or seaborn and improve its readability with color, annotations, and labels.

An ROC (Receiver Operating Characteristic) curve is a graphical representation that illustrates the relationship between a binary classification model's true positive rate and false positive rate at various threshold values. This curve is useful for comparing different models and identifying the ideal threshold that maximizes a model's sensitivity and specificity. ROC curves can be generated easily using libraries such as scikit-learn or matplotlib, and they can be complemented by the AUC (Area Under the Curve) score, which provides a summary of a model's overall quality.

A precision-recall curve is a graphical representation that illustrates the balance between precision and recall of a binary classification model at varying threshold levels. This curve is particularly valuable when assessing model performance in imbalanced datasets, where the positive class is either rare or more crucial than the negative class. Creating a precision-recall curve can be done effortlessly using libraries like scikit-learn or matplotlib, and it can be complemented by the AP (Average Precision) score, which provides a summary of the average precision across all thresholds.

A regression plot is a visual representation that displays the connection between the predicted and actual values of a regression model. It serves as a tool for evaluating the model's fit, bias, variance, detecting outliers, and identifying non-linear patterns. You can readily create a regression plot using libraries such as seaborn or matplotlib, and you have the option to enhance it with elements like a best-fit line, a confidence interval, or a residual plot for further analysis.

A feature importance plot is a visual representation that illustrates the significance of different features utilized by a machine learning model. It aids in comprehending how the model arrives at decisions and identifies the most relevant features for the prediction task. These plots can be easily generated using libraries like scikit-learn or xgboost and can be presented in various formats such as bar charts, pie charts, or tree maps.

Fluctuations in the learning curve, especially in validation or test set performance, reveal crucial aspects of a machine learning model's behavior. Abrupt changes may signal sensitivity to data variability or potential overfitting, while irregular oscillations could indicate challenges in optimization or a need for refined hyperparameter tuning. Inconsistent or unstable curves might imply insufficient training data, necessitating additional samples for improved generalization. Contextual analysis, considering specific data and model characteristics, is vital for interpreting these fluctuations and guiding adjustments in regularization, optimization algorithms, or data augmentation to enhance overall model performance and stability.

Ways to visualise the model performance and efficiency. - Learning Curve helps you to understand if your model is overfitting or underfitting based on the loss or metric trajectory for each iteration - Confusion matrix helps us understand the classification efficiency by the count of TPs and TNs of a binary classification model - AUC gives us the actual efficiency of a model over individual metrics like accuracy or precision etc as it provides importance to both negative and positive classes - SHAP and LIME helps us to understand importance of features and it's influence on predictions - Feature heatmap with correlation helps to remove highly correlated features as they lead to overfitting