Supervised Machine Learning

Supervised learning is a type of machine learning where a model learns from labelled data—meaning every input has a corresponding correct output. The model makes predictions and compares them with the true outputs, adjusting itself to reduce errors and improve accuracy over time. The goal is to make accurate predictions on new, unseen data. For example, a model trained on images of handwritten digits can recognise new digits it has never seen before.

Types of Supervised Learning in Machine Learning

Now, Supervised learning can be applied to two main types of problems:

• Classification: Where the output is a categorical variable (e.g., spam vs. non-spam emails, yes vs. no).
• Regression: Where the output is a continuous variable (e.g., predicting house prices, stock prices).

While training the model, data is usually split in the ratio of 80:20 i.e. 80% as training data and the rest as testing data. In training data, we feed input as well as output for 80% of data. The model learns from training data only. We use different supervised learning algorithms (which we will discuss in detail in the next section) to build our model. Let's first understand the classification and regression data through the table below:

Both the above figures have labelled data set as follows:

Figure A: It is a dataset of a shopping store that is useful in predicting whether a customer will purchase a particular product under consideration or not based on his/her gender, age and salary.

• Input: Gender, Age, Salary
• Output: Purchased i.e. 0 or 1; 1 means yes the customer will purchase and 0 means that the customer won't purchase it.

Figure B: It is a Meteorological dataset that serves the purpose of predicting wind speed based on different parameters.

• Input: Dew Point, Temperature, Pressure, Relative Humidity, Wind Direction
• Output: Wind Speed

Working of Supervised Machine Learning

The working of supervised machine learning follows these key steps:

1. Collect Labeled Data

• Gather a dataset where each input has a known correct output (label).
• Example: Images of handwritten digits with their actual numbers as labels.

2. Split the Dataset

• Divide the data into training data (about 80%) and testing data (about 20%).
• The model will learn from the training data and be evaluated on the testing data.

3. Train the Model

• Feed the training data (inputs and their labels) to a suitable supervised learning algorithm (like Decision Trees, SVM or Linear Regression).
• The model tries to find patterns that map inputs to correct outputs.

4. Validate and Test the Model

• Evaluate the model using testing data it has never seen before.
• The model predicts outputs and these predictions are compared with the actual labels to calculate accuracy or error.

5. Deploy and Predict on New Data

• Once the model performs well, it can be used to predict outputs for completely new, unseen data.

Supervised Machine Learning Algorithms

Supervised learning can be further divided into several different types, each with its own unique characteristics and applications. Here are some of the most common types of supervised learning algorithms:

• Linear Regression: Linear regression is a type of supervised learning regression algorithm that is used to predict a continuous output value. It is one of the simplest and most widely used algorithms in supervised learning.
• Logistic Regression: Logistic regression is a type of supervised learning classification algorithm that is used to predict a binary output variable.
• Decision Trees : Decision tree is a tree-like structure that is used to model decisions and their possible consequences. Each internal node in the tree represents a decision, while each leaf node represents a possible outcome.
• Random Forests: Random forests again are made up of multiple decision trees that work together to make predictions. Each tree in the forest is trained on a different subset of the input features and data. The final prediction is made by aggregating the predictions of all the trees in the forest.
• Support Vector Machine(SVM): The SVM algorithm creates a hyperplane to segregate n-dimensional space into classes and identify the correct category of new data points. The extreme cases that help create the hyperplane are called support vectors, hence the name Support Vector Machine.
• K-Nearest Neighbors: KNN works by finding k training examples closest to a given input and then predicts the class or value based on the majority class or average value of these neighbors. The performance of KNN can be influenced by the choice of k and the distance metric used to measure proximity.
• Gradient Boosting: Gradient Boosting combines weak learners, like decision trees, to create a strong model. It iteratively builds new models that correct errors made by previous ones.
• Naive Bayes Algorithm: The Naive Bayes algorithm is a supervised machine learning algorithm based on applying Bayes' Theorem with the “naive” assumption that features are independent of each other given the class label.

Let's summarize the supervised machine learning algorithms in table:

These types of supervised learning in machine learning vary based on the problem we're trying to solve and the dataset we're working with. In classification problems, the task is to assign inputs to predefined classes, while regression problems involve predicting numerical outcomes.

Practical Examples of Supervised learning

Few practical examples of supervised machine learning across various industries:

• Fraud Detection in Banking: Utilizes supervised learning algorithms on historical transaction data, training models with labeled datasets of legitimate and fraudulent transactions to accurately predict fraud patterns.
• Parkinson Disease Prediction: Parkinson’s disease is a progressive disorder that affects the nervous system and the parts of the body controlled by the nerves.
• Customer Churn Prediction: Uses supervised learning techniques to analyze historical customer data, identifying features associated with churn rates to predict customer retention effectively.
• Cancer cell classification: Implements supervised learning for cancer cells based on their features and identifying them if they are ‘malignant’ or ‘benign.
• Stock Price Prediction: Applies supervised learning to predict a signal that indicates whether buying a particular stock will be helpful or not.

Advantages

Here are some advantages of supervised learning listed below:

• Simplicity & clarity: Easy to understand and implement since it learns from labeled examples.
• High accuracy: When sufficient labeled data is available, models achieve strong predictive performance.
• Versatility: Works for both classification like spam detection, disease prediction and regression like price forecasting.
• Generalization: With enough diverse data and proper training, models can generalize well to unseen inputs.
• Wide application: Used in speech recognition, medical diagnosis, sentiment analysis, fraud detection and more.

Disadvantages

• Requires labeled data: Large amounts of labeled datasets are expensive and time-consuming to prepare.
• Bias from data: If training data is biased or unbalanced, the model may learn and amplify those biases.
• Overfitting risk: Model may memorize training data instead of learning general patterns, especially with small datasets.
• Limited adaptability: Performance drops significantly when applied to data distributions very different from training data.
• Not scalable for some problems: In tasks with millions of possible labels like natural language, supervised labeling becomes impractical.

Suggested Quiz

10 Questions

In supervised machine learning, what is the primary purpose of using labeled data during the training phase?

• A

  To introduce randomness in the model

• B

  To allow the model to learn the relationship between inputs and outputs

• C

  To increase the complexity of the model

• D

  To reduce the amount of data required for training

Explanation:

Because in Supervised Machine Learning Algorithm without properly labeled data, machine learning algorithms would struggle to understand the underlying patterns and make accurate predictions.

Which of the following algorithms is primarily used for classification tasks in supervised learning?

• A

  Linear Regression

• B

  K-Nearest Neighbors

• C

  Gradient Descent

• D

  Principal Component Analysis

Explanation:

KNN is a supervised Learning Algorithm used for classification task.

What is the main challenge associated with overfitting in supervised learning models?

• A

  The model performs well on unseen data

• B

  The model captures noise in the training data

• C

  The model requires less labeled data

• D

  The model has a high bias

Explanation:

The training data size is too small and does not contain enough data to accurate represent all input data values which lead to overfitting and noise

In the context of supervised learning, what is the role of hyperparameter tuning?

• A

  To randomly select features for the model

• B

  To adjust the model's parameters that are not learned during training

• C

  To eliminate the need for labeled data

• D

  To reduce the size of the training dataset

Explanation:

Hyperparameters are often used to tune the performance of a model, and they can have a significant impact on the model’s accuracy, generalization, and other metrics.

Which of the following metrics is NOT commonly used to evaluate the performance of a supervised learning model?

• A

  Accuracy

• B

  Recall

• C

  Mean Squared Error

• D

  Entropy

Explanation:

Entropy evaluation metrics is majorly used in Decision Trees

What is the primary difference between classification and regression tasks in supervised learning?

• A

  Classification predicts categorical outcomes, while regression predicts continuous outcomes

• B

  Classification requires more data than regression

• C

  Regression is always more complex than classification

• D

  Classification can only be performed on numeric data

Explanation:

Classification is used for categorical output and regression is used for continuous outcome

In supervised learning, what is the purpose of splitting the dataset into training and test sets?

• A

  To increase the overall size of the dataset

• B

  To evaluate the model's performance on unseen data

• C

  To ensure all data points are used in training

• D

  To reduce the complexity of the model

Explanation:

The purpose of splitting data is to test the model performance after training.

Which supervised learning algorithm is particularly known for its ability to handle both classification and regression tasks?

• A

  Decision Trees

• B

  K-Nearest Neighbors

• C

  Naive Bayes

• D

  Linear Regression

Explanation:

Decision trees can used for both classification and regression task

What is the main advantage of using ensemble methods like Random Forests in supervised learning?

• A

  They simplify the model-building process

• B

  They reduce the likelihood of overfitting by combining multiple models

• C

  They require less computational power

• D

  They eliminate the need for labeled data

Explanation:

Random Forests average the predictions of many decision trees, which decreases the chances of overfitting the data.

Which of the following statements correctly describes the concept of bias in supervised learning models?

• A

  Bias refers to the model's inability to learn from the training data

• B

  Bias is the error introduced by approximating a real-world problem with a simplified model

• C

  Bias only affects the performance of regression models

• D

  Bias is always desirable in model training

Explanation:

In Supervised learning bias refers to the error introduced by approximating a real-world problem (which may be complex) with a simplified model.

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