Supervised learning is a way for computers to learn from examples. It mainly includes two types: classification and regression. Each type has its own real-world uses.
Email Spam Detection: Classification helps identify whether an email is 'spam' or 'not spam.' Programs like Naive Bayes and Support Vector Machines are used for this. A report says that more than 85% of email traffic is spam, showing how important it is to sort emails correctly.
Medical Diagnosis: Some machine learning models can look at medical images or patient data to help doctors identify diseases. For example, deep learning methods can find diabetic retinopathy in eye scans with over 95% accuracy.
Sentiment Analysis: Classification also helps us understand how people feel about things on social media, like Twitter. Techniques such as logistic regression can accurately guess people's feelings in tweets up to 85% of the time.
House Price Prediction: Regression is used to guess how much houses will cost. For instance, databases can help estimate real estate prices based on factors like location and size. In 2021, the average home price in the U.S. was about $347,500.
Stock Price Forecasting: Machine learning methods, like time-series regression, can predict how stock prices will change in the future. Some research shows that these models can be about 70% accurate in unpredictable markets.
Sales Forecasting: Businesses often use regression to estimate future sales based on past data. This helps them manage inventory better, often reducing extra stock by 25%.
In short, both classification and regression are very important in many areas. They help improve decision-making by using data to support choices.
Supervised learning is a way for computers to learn from examples. It mainly includes two types: classification and regression. Each type has its own real-world uses.
Email Spam Detection: Classification helps identify whether an email is 'spam' or 'not spam.' Programs like Naive Bayes and Support Vector Machines are used for this. A report says that more than 85% of email traffic is spam, showing how important it is to sort emails correctly.
Medical Diagnosis: Some machine learning models can look at medical images or patient data to help doctors identify diseases. For example, deep learning methods can find diabetic retinopathy in eye scans with over 95% accuracy.
Sentiment Analysis: Classification also helps us understand how people feel about things on social media, like Twitter. Techniques such as logistic regression can accurately guess people's feelings in tweets up to 85% of the time.
House Price Prediction: Regression is used to guess how much houses will cost. For instance, databases can help estimate real estate prices based on factors like location and size. In 2021, the average home price in the U.S. was about $347,500.
Stock Price Forecasting: Machine learning methods, like time-series regression, can predict how stock prices will change in the future. Some research shows that these models can be about 70% accurate in unpredictable markets.
Sales Forecasting: Businesses often use regression to estimate future sales based on past data. This helps them manage inventory better, often reducing extra stock by 25%.
In short, both classification and regression are very important in many areas. They help improve decision-making by using data to support choices.