Podcast
Questions and Answers
What is likely to happen to a model that is overfitting?
What is likely to happen to a model that is overfitting?
Which situation describes underfitting?
Which situation describes underfitting?
Which metric is most suitable to assess a model's performance for predicting a continuous variable?
Which metric is most suitable to assess a model's performance for predicting a continuous variable?
Which of the following statements about Mean Squared Error (MSE) is correct?
Which of the following statements about Mean Squared Error (MSE) is correct?
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What is a characteristic of Root Mean Squared Error (RMSE) compared to Mean Absolute Error (MAE)?
What is a characteristic of Root Mean Squared Error (RMSE) compared to Mean Absolute Error (MAE)?
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What does Mean Absolute Error (MAE) specifically measure?
What does Mean Absolute Error (MAE) specifically measure?
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In which situation would you prefer using MAE over Mean Squared Error (MSE)?
In which situation would you prefer using MAE over Mean Squared Error (MSE)?
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How is Root Mean Squared Error (RMSE) related to Mean Squared Error (MSE)?
How is Root Mean Squared Error (RMSE) related to Mean Squared Error (MSE)?
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Which of the following statements about MAE is correct?
Which of the following statements about MAE is correct?
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Which scenario would indicate a potential outlier in real estate data?
Which scenario would indicate a potential outlier in real estate data?
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What is a main characteristic of Mean Squared Error (MSE)?
What is a main characteristic of Mean Squared Error (MSE)?
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If a dataset has a large outlier, how might this affect the RMSE compared to MAE?
If a dataset has a large outlier, how might this affect the RMSE compared to MAE?
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When evaluating the performance of a regression model, what role does MAE play?
When evaluating the performance of a regression model, what role does MAE play?
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What is the primary consequence of overfitting a model?
What is the primary consequence of overfitting a model?
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Which metric is most sensitive to outliers in regression analysis?
Which metric is most sensitive to outliers in regression analysis?
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In the context of regression models, which approach helps mitigate overfitting?
In the context of regression models, which approach helps mitigate overfitting?
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What is the main advantage of using Root Mean Squared Error (RMSE) over Mean Square Error (MSE)?
What is the main advantage of using Root Mean Squared Error (RMSE) over Mean Square Error (MSE)?
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Which of the following techniques is commonly used to combat underfitting in a model?
Which of the following techniques is commonly used to combat underfitting in a model?
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What does Mean Absolute Error (MAE) measure in a regression context?
What does Mean Absolute Error (MAE) measure in a regression context?
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What is a key characteristic of underfitting in a predictive model?
What is a key characteristic of underfitting in a predictive model?
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Which of the following statements about cross-validation is true?
Which of the following statements about cross-validation is true?
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What does R-squared (R²) specifically indicate about a regression model?
What does R-squared (R²) specifically indicate about a regression model?
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When is it most appropriate to use Mean Squared Error (MSE) as a metric?
When is it most appropriate to use Mean Squared Error (MSE) as a metric?
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Which metric is often used when the scale of data varies widely across different samples?
Which metric is often used when the scale of data varies widely across different samples?
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What does Mean Squared Error (MSE) primarily help to indicate?
What does Mean Squared Error (MSE) primarily help to indicate?
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What does Root Mean Squared Error (RMSE) express in relation to predictions?
What does Root Mean Squared Error (RMSE) express in relation to predictions?
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Which of the following metrics provides information about the average relative error?
Which of the following metrics provides information about the average relative error?
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What is the implication of an R-squared value of 1 in a regression model?
What is the implication of an R-squared value of 1 in a regression model?
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Which metric is not primarily used for regression analysis?
Which metric is not primarily used for regression analysis?
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Which of these metrics would likely show high values in the presence of outliers?
Which of these metrics would likely show high values in the presence of outliers?
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What does a decrease in RMSE over iterations suggest about a model's performance?
What does a decrease in RMSE over iterations suggest about a model's performance?
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If the Mean Absolute Error (MAE) is low, what can be inferred about the model's predictions?
If the Mean Absolute Error (MAE) is low, what can be inferred about the model's predictions?
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Which metric is sensitive to outliers and does not provide results in the original unit?
Which metric is sensitive to outliers and does not provide results in the original unit?
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What is a primary advantage of using Root Mean Squared Error (RMSE) over Mean Absolute Error (MAE)?
What is a primary advantage of using Root Mean Squared Error (RMSE) over Mean Absolute Error (MAE)?
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Which of the following metrics provides a percentage error making it easy to interpret?
Which of the following metrics provides a percentage error making it easy to interpret?
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What is a disadvantage of using R-squared (R2) as a performance metric?
What is a disadvantage of using R-squared (R2) as a performance metric?
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Which performance metric is particularly suited for forecasting and time series analysis?
Which performance metric is particularly suited for forecasting and time series analysis?
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How does Mean Absolute Error (MAE) perform compared to Mean Squared Error (MSE) concerning outliers?
How does Mean Absolute Error (MAE) perform compared to Mean Squared Error (MSE) concerning outliers?
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Which metric would you choose if you want to assess the average percentage error, particularly for fluctuating data near zero?
Which metric would you choose if you want to assess the average percentage error, particularly for fluctuating data near zero?
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In regression analysis, which metric would indicate a good fit to the data while being interpretable in its original unit?
In regression analysis, which metric would indicate a good fit to the data while being interpretable in its original unit?
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Which of the following is NOT a characteristic of R-squared (R2)?
Which of the following is NOT a characteristic of R-squared (R2)?
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Which metric treats positive and negative errors the same regardless of their magnitude?
Which metric treats positive and negative errors the same regardless of their magnitude?
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Study Notes
Performance Metrics in Machine Learning
- Francis Wolinski, Associate Professor and Director of the MSc Artificial Intelligence for Business Transformation at SKEMA Business School, presented on performance metrics in machine learning.
- The presentation is for the PGE M1 – Understanding Al in Business Context course at SKEMA Business School, scheduled for October 28/10/2024 in Paris.
Subject Introduction
- A short video illustrating AI failure in a soccer game was used to introduce the subject.
- The AI camera mistakenly identified a referee's bald head as a ball in a soccer game.
Agenda
- The agenda outlines topics such as machine learning basics, different algorithms, supervised machine learning methodologies, underfitting/overfitting, performance metrics for regression and binary classification, fairness metrics, conclusion, and perspectives.
What is Machine Learning?
- Machine learning algorithms create models from sample data (training data) to make predictions or decisions without explicit programming.
- In traditional programming, a program is given data as input, processes it, and outputs a result.
- Machine learning builds a model from data and then uses that model to predict or decide on new, unseen data.
Example: Escape the Maze
- Traditional programming solves a maze using logic rules and steps (e.g., turn left, move forward.)
- Machine learning involves training a system to navigate mazes.
Example of Traditional Programming
- The program provides step-by-step instructions to navigate the maze based on data inputs.
- Specific code will use conditional statements (if/else).
Example of Machine Learning
- Machine-learning algorithms learn how to navigate a maze by trial and error.
- The process involves showing the system input (the maze) and the desired output (route to the exit).
Artificial Intelligence, Machine Learning, and Deep Learning
- Artificial intelligence (AI) encompasses various techniques; machine learning (ML) and deep learning (DL) are subfields.
- Machine learning algorithms learn from data without explicit programming, while deep learning structures exploit numerous layers to extract complex patterns.
Attention Is All You Need
- This paper proposes a new network architecture, the Transformer, relying solely on attention mechanisms, achieving state-of-the-art results in machine translation tasks.
- The Transformer network is more parallelizable and requires less time to train compared to previous models.
- The Transformer excels in various machine translation tasks compared to previous models. The results are good with even limited training data.
Performance Metrics in Machine Learning
- Performance metrics evaluate the model's strength and weaknesses. Performance metrics include Accuracy, Precision, Recall, F1 score, MSE, RMSE, MAE, etc.
Supervised Machine Learning
- Input data and target values are provided
- The model predicts values of "y" (target) based on features of "x."
- Different methodologies include splitting data into training and test sets to evaluate the model's ability to generalize (predict correctly on new, unseen data).
- Regression models predict continuous values, while classification models predict discrete classes.
Supervised Machine Learning: Regression
- Predicting a continuous variable associated with an object.
- Examples include stock market prediction, energy consumption prediction, and real estate price prediction.
Supervised Machine Learning: Classification
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Identifying the category an object belongs to.
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Examples include customer segmentation, image recognition, and disease diagnosis.
A few examples of Use
- These include stock market prediction, sales forecasting, and demand forecasting. There are examples based on real-world scenarios that involve machine learning regression.
Business Applications of Machine Learning
- Machine learning is used for prediction in real-world scenarios (e.g., risk assessment in insurance or sales forecasting.)
Underfitting and Overfitting (Bias and Variance)
- Underfitting occurs when a model is too simple to learn the data patterns; high bias, poor performance on training and test sets.
- Overfitting occurs when a model is too complex, learning the data noise; high variance, poor performance on test sets.
What are the advantages of pre-processing and cleaning data before modeling?
- Data quality improves model performance, reduces bias, ensures computational efficiency, and leads to improved model interpretability.
- Addresses data quality aspects such as errors, outliers, or missing values.
Performance Metrics in Regression
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Metrics measure the model's ability to predict values.
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Mean squared error (MSE) and Root mean squared error (RMSE) penalize large errors, while mean absolute error (MAE) is less sensitive to outliers.
Performance Metrics in Binary Classification
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Includes metrics like accuracy, precision, recall, F1-score, specificity, and AUC-ROC.
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Using a contingency table, specific classifications (true positive, true negative, false positive, false negative) are utilized for analysis.
Fairness Metrics
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Fairness Metrics evaluate whether classification models show bias toward certain groups.
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Methods like Bare Rate, Demographic Parity, Equal Odds, and Equal Opportunity analyze prediction rates across different demographic groups.
Conclusion
- In the end, it's important to choose several metrics, identify what the limitations are, consider the consequences for misclassifications to have a comprehensive understanding.
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