3. Major Challenges Machine Learning Faces
There are several major challenges that machine learning faces. Some of the most significant challenges include:
Data Quality: Machine learning relies heavily on data, and the quality of the data can greatly affect the performance of the model. Issues such as missing or corrupted data, outliers, and bias can all negatively impact the accuracy of the model. For instance, in healthcare, missing or incorrect data in patient records can impact the performance of ML models in predicting patient outcomes, such as disease diagnosis and prognosis.
Overfitting: Overfitting occurs when a model becomes too complex and starts to fit the noise in the data rather than the underlying pattern. This can lead to poor performance on new, unseen data. For example, an overfitting machine learning model that predicts stock prices can result in poor predictions in volatile markets or during market crashes.
Underfitting: When a selected machine learning model is too simple for a complex problem, it cannot learn patterns and behaviors in data, and would result in poor metric results.
For instance, assume that you use a simple linear regression model to predict house prices, where the predicted housing price is a linear combination of the square footage and the number of bedrooms. However, in reality, the relationship between housing prices and these features is much more complex and influenced by other factors such as location, age of the house, and quality of construction. In this case, the model would not be flexible enough to capture all the variations in the data and would oversimplify the relationship between the features and target variable.
Generalization: Machine learning models are trained on a specific dataset, but they need to generalize well on unseen data. Generalization is a major concern in machine learning, as a model that performs well on the training dataset may not perform well on unseen data.
Scalability: As the amount of data increases, the computational resources needed to train and run machine learning models also increases. This can make it difficult to scale machine learning models to handle very large datasets. For instance, autonomous drivers need real-time predictions to ensure safety, making computational complexity a major challenge.
Explainability: Many machine learning models, particularly deep learning models, can be difficult to interpret and understand. This can make it challenging to explain the reasoning behind a model's predictions and to identify potential errors in the model. Interpretable models are needed to ensure that the models comply with legal and ethical requirements in many real-life applications.
Feature Selection: In many cases, the data contains many features and it's hard to select the relevant ones to be used in the model. This can lead to poor performance and overfitting issues. Consider a case when you want to predict monthly total sales for a store. This outcome can depend on numerous variables such as total number of customers per month, inflation rate, and even weather. Filtering out unnecessary features to achieve faster results can be a cumbersome challenge.
Human bias: Human bias can be introduced in the data collection process, data annotation, feature selection or even in the model development process. For example, in criminal justice, biased predictions can result in unfair outcomes, such as discrimination against certain communities.
Data Quality: Machine learning relies heavily on data, and the quality of the data can greatly affect the performance of the model. Issues such as missing or corrupted data, outliers, and bias can all negatively impact the accuracy of the model. For instance, in healthcare, missing or incorrect data in patient records can impact the performance of ML models in predicting patient outcomes, such as disease diagnosis and prognosis.
Overfitting: Overfitting occurs when a model becomes too complex and starts to fit the noise in the data rather than the underlying pattern. This can lead to poor performance on new, unseen data. For example, an overfitting machine learning model that predicts stock prices can result in poor predictions in volatile markets or during market crashes.
Underfitting: When a selected machine learning model is too simple for a complex problem, it cannot learn patterns and behaviors in data, and would result in poor metric results.
For instance, assume that you use a simple linear regression model to predict house prices, where the predicted housing price is a linear combination of the square footage and the number of bedrooms. However, in reality, the relationship between housing prices and these features is much more complex and influenced by other factors such as location, age of the house, and quality of construction. In this case, the model would not be flexible enough to capture all the variations in the data and would oversimplify the relationship between the features and target variable.
Generalization: Machine learning models are trained on a specific dataset, but they need to generalize well on unseen data. Generalization is a major concern in machine learning, as a model that performs well on the training dataset may not perform well on unseen data.
Scalability: As the amount of data increases, the computational resources needed to train and run machine learning models also increases. This can make it difficult to scale machine learning models to handle very large datasets. For instance, autonomous drivers need real-time predictions to ensure safety, making computational complexity a major challenge.
Explainability: Many machine learning models, particularly deep learning models, can be difficult to interpret and understand. This can make it challenging to explain the reasoning behind a model's predictions and to identify potential errors in the model. Interpretable models are needed to ensure that the models comply with legal and ethical requirements in many real-life applications.
Feature Selection: In many cases, the data contains many features and it's hard to select the relevant ones to be used in the model. This can lead to poor performance and overfitting issues. Consider a case when you want to predict monthly total sales for a store. This outcome can depend on numerous variables such as total number of customers per month, inflation rate, and even weather. Filtering out unnecessary features to achieve faster results can be a cumbersome challenge.
Human bias: Human bias can be introduced in the data collection process, data annotation, feature selection or even in the model development process. For example, in criminal justice, biased predictions can result in unfair outcomes, such as discrimination against certain communities.
Updated on: 30/01/2023
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