Here's how you can assess the performance of a machine learning model.

In machine learning, performance of model can be evaluated using metrics. For classification and regression,we have different metrics. For classification, we use accuracy, precision, recall, F1 score, AUC and ROC, True positive rate, False positive rate and Confusion Matrix. For regression, we use Mean Square error(MSE), absolute mean square error, R2 score, Adjusted R2 score. By using these metrics, we can evaluated the performance of model.

Choice of Performance metrics for machine learning algorithms depends upon the nature of problems. For a regression machine learning algorithms, Mean Absolute Error (MAE),Mean Squared Error (MSE),Root Mean Squared Error (RMSE),R² score and Adjusted R² are used to assess the performance of ML algorithms. Mostly R2 score and Adjusted R2 is the main criteria among these metrics to evaluate the performance among all. For a classification problem, Accuracy, Confusion Matrix, Precision and Recall, F1-score, AU-ROC are used to assess the performance of ML algorithms. Here Accuracy is mostly prefered to evaluate the performance of ML algorithms.

Prior to delving into model evaluation, it's imperative to establish the metrics that will gauge the success of your model. For classification conundrums, accuracy, precision, recall, and the F1 score are prevalent choices. Accuracy gauges the proportion of correct predictions, whereas precision and recall spotlight the model's aptitude in identifying positive cases. The F1 score strikes a balance between precision and recall. In regression scenarios, mean absolute error (MAE) and root mean square error (RMSE) are favored metrics, delineating the average prediction error in the units of the target variable.

These metrics simply put are..... Accuracy: How often the model is correct. Precision: How accurate the model is when it makes a prediction. Recall: How well the model detects all instances of a condition. F1 Score: Harmonic mean of precision and recall. Mean Squared Error (MSE): How close the model's predictions are to the actual values. R-Squared: How well the model explains the data. These metrics help us understand how our model is performing and where we need to improve it. By using a combination of these metrics, we can get a comprehensive understanding of our model's performance and make informed decisions to optimize it.

Accuracy, Precision, Recall, and F1-Score Confusion Matrix: Provides a detailed breakdown of true positives, false positives, true negatives, and false negatives. ROC-AUC: The Receiver Operating Characteristic curve and Area Under the Curve measure the model's ability to distinguish between classes. Cross-Validation: Splits the data into multiple subsets to ensure the model performs well across different data segments. Mean Absolute Error (MAE) and Mean Squared Error (MSE): Evaluate the average error in regression tasks.

Splitting data in training and testing subsets is an important steps towards building a machine learning model. It is completely your choice that you can train your model on 80% data or 90% data etc. and test the model on rest of data. Sometimes data is also divided into subsets as 80% part training, 10% part testing and 10% part for validation for machine learning model. Along with train-test split, k-fold cross validation also used, The model is trained and evaluated k times using a different fold as the testing set each time. Performance metrics from each fold are averaged to estimate the model's overall performance . It leads to build a good machine learning model.

A pivotal aspect of evaluating a machine learning model is assessing its performance on unseen data. Enter data splitting. Typically, you'd segment your dataset into a training set and a test set, frequently adopting a 70-30 or 80-20 split. The training set serves as the classroom for the model, whereas the test set scrutinizes its performance. Cross-validation presents another stratagem: the dataset is partitioned into folds, and the model undergoes multiple training and testing iterations, with each fold serving as the test set in turn, ensuring resilience and reliability.

When deciding on a data split, consider the size of your dataset. For large datasets (thousands to millions of samples), use 80% for training, 10% for testing, and 10% for validation. For medium datasets (hundreds to thousands of samples), use 70% for training, 20% for testing, and 10% for validation. For small datasets (less than 10000 samples), use 60% for training, 30% for testing, and 10% for validation. And for very small datasets (fewer than 1000 samples), use 50% for training and 50% for testing. These guidelines can be adjusted based on your specific problem and model requirements.

Há meio que uma receita de bolo quando se trata de divisão dos dados para treinamento e validação de desempenho. O que deve também levar em consideração é o tamanho da amostra. De nada adianta dividir em 70-30 sendo que você possui uma amostra com poucos registros. Nesse caso, muitas vezes, o acompanhamento com dados reais seja mais adequado do que tirar 30% de grau de liberdade de seus dados.

In the realm of classification quandaries, behold the confusion matrix—a veritable gem. It's a tabular representation contrasting actual versus predicted values, affording a glimpse into true positives, false positives, true negatives, and false negatives. This trove of insights is pivotal for computing refined performance metrics like precision and recall. Crafting a confusion matrix is a breeze with code libraries such as scikit-learn; just summon it with a straightforward function call: from sklearn.metrics import confusion_matrix.

A matriz de confusão oferece uma visão clara sobre onde o modelo está acertando e onde está cometendo erros. Ferramentas como a biblioteca scikit-learn facilitam a criação de uma matriz de confusão com uma função simples: from sklearn.metrics import confusion_matrix. Grande ponto que sua principal contribuição é em encontrar falsos positivos e suas nuances. Não é um ponto negativo caso a matriz não tenha gerado o resultado que era esperado, só aponta que é necessário ajustes, mas que pode dar continuidade as outras métricas de avaliação.

ROC curve plots the performance of a classification model. Read it by starting from the top left corner, where the True Positive Rate (TPR) is high and the False Positive Rate (FPR) is low. Move right, and the TPR decreases while the FPR increases. A good model has a curve that stays high (TPR) and low (FPR) for a long time, then drops quickly. The closer the curve is to the top left corner, the better the model is at distinguishing true positives from false positives. The area under the curve (AUC) measures the model's overall performance, with higher values indicating better performance.

Parecido com a matriz de confusão, o objetivo é avaliar modelos binários (de classificação), onde a integral sobre sua curva ROC (AUC) oferece uma medida única de desempenho geral, agregando os resultados em todos os limiares de classificação possíveis.

Feast your eyes upon the Receiver Operating Characteristic (ROC) curve—an emblem of discernment in the binary classifier realm. This graphical marvel depicts the diagnostic prowess of a classifier system by juxtaposing the true positive rate (TPR) against the false positive rate (FPR) across various threshold settings. Behold, the area under this curve (AUC), a singular metric encapsulating overall performance, amalgamating outcomes across all conceivable classification thresholds. A model showcasing flawless discrimination boasts an AUC of 1, while one bereft of discriminatory prowess hovers at an AUC of 0.5.

Behold the learning curves, intricate plots that unveil the model's journey through the realms of training and validation sets, as time progresses or the number of training instances unfolds. These visual marvels serve as diagnostic tools, shedding light on afflictions such as overfitting and underfitting. Witness overfitting, wherein the model thrives within the confines of the training data but falters when faced with unseen realms. And lo, underfitting, a plight where the model's simplicity fails to capture the nuances of the underlying data trends. Delve into the depths of learning curves to discern whether infusing more data or adjusting model complexity might bestow upon your creation the mantle of enhanced performance.

O overfitting ocorre quando o modelo tem um excelente desempenho nos dados de treinamento, mas um desempenho ruim nos dados não vistos. O underfitting acontece quando o modelo é muito simples e não consegue capturar as tendências subjacentes nos dados. Analisando as curvas de aprendizado, você pode identificar se é necessário adicionar mais dados ou ajustar a complexidade do modelo para melhorar seu desempenho

Feature selection is like picking the most important ingredients for a recipe, keeping only the essential ones and leaving out the unnecessary ones, which might have a disruptive effect on the models Accuracy. - Correlation-based feature selection: Picks features that are highly correlated with the target variable and not too correlated with each other - Recursive Feature Elimination (RFE): Keeps eliminating the least important features until you're left with the best ones - LASSO (Least Absolute Shrinkage and Selection Operator): Uses some fancy math to make unimportant features disappear - Random Forest Feature Importance: Uses a random forest model to figure out which features are the most important. This is very effective.

One more can be added which is Cross Validation. It is a method in which we do not use the whole dataset for training. In this technique, some part of the dataset is reserved for testing the model. There are many types of Cross-Validation out of which K Fold Cross Validation is mostly used. In K Fold Cross Validation the original dataset is divided into k subsets. The subsets are known as folds. This is repeated k times where 1 fold is used for testing purposes. Rest k-1 folds are used for training the model. So each data point acts as a test subject for the model as well as acts as the training subject. This technique generalizes the model well and reduces the error rate

There are several metrics as mentioned above in this thread to assess the machine learning model, However, in my opinion, what is more important and crucial is testing different scenarios which are not in your training data set but some what align with it. It is important for you to have enough test data set depicting various scenarios. One way to create such data sets is to generate synthetic data!