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Last updated on 2024年5月9日

How can cross-validation techniques improve your model's fit quality?

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Cross-validation is a robust technique in data science that helps ensure the model you build generalizes well to new, unseen data. When you're tasked with creating predictive models, it's crucial to assess their performance accurately. Cross-validation allows you to do just that, by partitioning your data into subsets, training the model on some of those subsets, and validating it on the remaining ones. This process helps mitigate the risk of overfitting, where a model performs exceptionally well on the training data but poorly on new data. By using cross-validation, you can fine-tune your model's complexity and select the one that achieves the best performance balance.

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1 Understanding CV

Cross-validation (CV) is a method used to evaluate the generalizability of your model by dividing the dataset into several parts. Typically, the data is split into 'folds', and for each iteration, a different fold serves as the test set while the remaining data is used for training. This process repeats until each fold has been used as the test set. CV helps in assessing how well your model will perform on unseen data, providing a more accurate measure of its predictive power compared to using a single train-test split.

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Bhaskar Manoj

Manager-Analytics at Union Bank of India | CAIIB | Co Chair-GTI | GGI | IIM-I
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Cross validation (CV) is a method that aims to improve the "generalization" of an ML model, i.e. the ability of the model to perform on test data. Although there are different kinds of cross-validation techniques such as K-Fold CV, Stratified K-Fold CV, Leave-p-out CV, essentially the underlying principle is resampling without replacement. To understand, consider K-Fold Cross Validation. Based on the specified folds (k), the training data is split into k-folds such that k-1 folds are used for training and 1 fold is used for validation. The same process is repeated k times by resampling the training data. Key point to remember is that unlike in bootstrapping, the folds/subsets created in this process do not contain repeated data points.

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Gabriel Guilherme

Data Analyst | Business Intelligence | Economist
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Como ele generaliza os dados, dividindo eles em folds, e depois de treinado, ele usa tanto os dados de testes como de treino para verificar o comportamento da predi??o, é uma ferramenta inteligente para estressar seu modelo final, trazendo uma maior confian?a, e também, para mitigar o overfitting.

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2 Benefits of CV

One of the main benefits of cross-validation is its ability to reduce overfitting, which occurs when a model learns the noise in the training data rather than the underlying pattern. By exposing the model to multiple train-test splits, you can ensure that it learns to generalize from the data rather than memorizing it. Additionally, cross-validation can help you select the right model and parameters by providing a comprehensive view of how model performance varies with different training sets.

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Shesh Narayan Gupta

Manager Data Science at Discover Financial Services | Data Scientist | Machine Learning | Data Analyst | Research | Product Manager | Business Analyst | Python | SQL | Tableau | RPA | Hadoop | 10x Azure
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Firstly, they provide a robust estimate of a model's performance by evaluating its generalization ability across multiple subsets of the data. By training and testing the model on different partitions of the dataset, CV helps detect overfitting and ensures that the model's performance is not overly optimistic. Moreover, it enhances the utilization of available data, maximizing the amount of information used for training and validation. CV allows for the identification of hyperparameters that generalize well across different data splits, leading to more stable and reliable model performance. CV techniques serve as a critical tool for validating and fine-tuning machine learning models, ultimately improving their fit quality and predictions.

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Gabriel Guilherme

Data Analyst | Business Intelligence | Economist
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Um exemplo que já pude observar é, usar o CV para analisar o mesmo algoritmo de classifica??o, porém com pesos diferentes. Assim, o CV atua em camadas de diferentes pesos estatísticos, mas no mesmo modelo. Podemos observar que nem sempre, o melhor algoritmo de classifica??o é o mais complexo. CV auxilia muito bem nesse quesito.

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3 Types of CV

Several types of cross-validation cater to different scenarios. The most common type is k-fold cross-validation, where 'k' is the number of folds the data is divided into. There's also stratified k-fold cross-validation, which is used for datasets with an uneven class distribution to ensure each fold maintains the proportion of classes. Leave-one-out cross-validation is another type, useful for small datasets, where each instance becomes a test set once. Understanding these types can help you choose the most appropriate one for your data.

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Gabriel Guilherme

Data Analyst | Business Intelligence | Economist
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Diferentes tipos de abordagem, porém o mesmo objetivo teórico. N?o existe uma resposta, ou melhor, uma receita de bolo. Tudo depende de como seus dados se comportam, para isso, é necessário toda uma abordagem estatística antes, compreens?o de negócio e objetivos claros para que ent?o, só no final da cadeia, você possa escolher o mesmo meio de validade sua hipótese.

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4 Implementing CV

Implementing cross-validation in your modeling process can be straightforward with libraries like scikit-learn in Python. Here's a simple example using k-fold cross-validation:

from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
# Initialize classifier
clf = RandomForestClassifier()
# Perform 5-fold cross-validation
scores = cross_val_score(clf, X, y, cv=5)
print("Accuracy scores for each fold:", scores)

This snippet initializes a random forest classifier and evaluates it using 5-fold cross-validation.

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Gabriel Guilherme

Data Analyst | Business Intelligence | Economist
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from sklearn.model_selection import LeaveOneOut from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=7) loo = LeaveOneOut() model = LogisticRegression() scores = [] for train_index, test_index in loo.split(X): X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] model.fit(X_train, y_train) y_pred = model.predict(X_test) scores.append(accuracy_score(y_test, y_pred)) average_score = sum(scores) / len(scores) print(f'Média dos scores: {average_score}')

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5 Tuning with CV

Cross-validation isn't just for evaluating models; it's also pivotal for tuning hyperparameters. By running CV with different hyperparameter values, you can determine which settings lead to the best model performance. This methodical approach to tuning avoids the pitfall of choosing hyperparameters that only perform well on a specific dataset partition, leading to more robust models.

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Gabriel Guilherme

Data Analyst | Business Intelligence | Economist
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Hiperparametros s?o configura??es que n?o s?o aprendidas diretamente dentro dos estimadores. Em machine learning, o tuning é crucial porque os hiperparametros podem controlar a complexidade do modelo e afetar o desempenho do mesmo.

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6 CV in Practice

In practice, cross-validation is an indispensable tool in your data science toolkit. It provides a more reliable estimate of model performance than a simple train-test split, especially in cases where data is limited or imbalanced. By iteratively training and testing on different subsets of data, you can identify and correct for potential weaknesses in your model, ensuring it performs well when deployed in real-world scenarios.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can cross-validation techniques improve your model's fit quality?

1

2

3

4

5

6

7

1 Understanding CV

2 Benefits of CV

3 Types of CV

4 Implementing CV

5 Tuning with CV

6 CV in Practice

7 Here’s what else to consider

Data Science

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How can cross-validation techniques improve your model's fit quality?

1

2

3

4

5

6

7

1 Understanding CV

2 Benefits of CV

3 Types of CV

4 Implementing CV

5 Tuning with CV

6 CV in Practice

7 Here’s what else to consider

Data Science

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