Is Machine Learning Supervised or Unsupervised?

In modern technology, machine learning has become a cornerstone, shaping the ways we interact with the world. Machine learning powers many applications we use every day, from recommendation systems to self-driving cars. Is Machine Learning supervised or unsupervised? In this article we’re going to break down the various types of machine learning, look at some common algorithms, and see where supervised learning and unsupervised learning sit in the picture.

What is Machine Learning?

Just before answering is machine learning supervised or unsupervised, let’s understand what actually is machine learning.

Machine Learning is Google NLU category which is useful for Natural Language Understanding machine learning algorithms to learn from data automatically, and getting better in time without human intervention. Machine learning aims to create models that can predict or decide based on data.

There are two primary types of machine learning:

1. Supervised Learning
2. Unsupervised Learning

These approaches differ in how they learn from data.

Supervised Learning

One of the widely used techniques in machine learning is supervised learning. Supervised learning is where the model is trained with labeled data. This means that the algorithm is trained on a dataset where it gets both the input data and the accurate labels on output.

Key Characteristics of Supervised Learning:

• Labeled Data: The training data includes the correct answers (labels).
• Training: Training the model on data to learn the relationship between input (features) and output (targets).
• For example, predicting house prices based on size, location, and number of rooms.

Steps in Supervised Learning:

1. Data Collection: Gather a dataset that includes both inputs and labels.
2. Data Preprocessing: Clean and prepare the data for training.
3. Model Selection: Choose an appropriate machine learning model (e.g., linear regression, decision trees).
4. Model Training: Train the model using labeled data.
5. Evaluation: Evaluate the model’s performance using testing data.
6. Prediction: Use the trained model to make predictions on new data.

Common Supervised Learning Algorithms:

• Support Vector Machine
• K-Nearest Neighbors
• Random Forest
• Linear Regression

Unsupervised Learning

Unlike supervised learning, unsupervised learning trains a model with unlabeled data. Now, here the algorithm attempts to discover patterns and structures in data without pre-labeled output.

Key Characteristics of Unsupervised Learning:

• Unlabeled Data: The data has no labels, and the model tries to infer the relationships within the data.
• Training: Training the model on data to learn the relationship between input (features) and output (targets).
• For example, predicting house prices based on size, location, and number of rooms.

Steps in Unsupervised Learning:

1. Data Collection: Gather a dataset that doesn’t have labels.
2. Data Preprocessing: Clean and prepare the data.
3. Model Selection: Choose an appropriate machine learning model (e.g., clustering, dimensionality reduction).
4. Model Training: Train the model to find patterns or clusters in the data.
5. Evaluation: Assess the quality of the results (e.g., cluster coherence, dimensionality reduction effectiveness).

Common Unsupervised Learning Algorithms:

• Clustering types of Algorithms (e.g. K-Means, DBSCAN)
• Also Read: Dimensionality Reduction (PCA, t-SNE, etc.)
• Association Algorithms (Apriori, etc.)

SVM in Machine Learning: Supervised or Unsupervised?

SVM is a supervised learning algorithm. It is employed mostly for classification and regression work. SVM attempts to find the best separating hyperplane that separates the classes in the feature space.

Why SVM is Supervised:

• SVM requires labeled data for training.
• It uses the input-output pairs to find the best decision boundary.

Recommendation System in Machine Learning: Supervised or Unsupervised?

A recommendation system can be either supervised or unsupervised, depending on the approach used.

Supervised Recommendation Systems:

• When recommendation systems use labeled data, like user ratings or preferences, they fall under supervised learning.
• Example: Collaborative filtering based on known ratings is a supervised method.

Unsupervised Recommendation Systems:

• If recommendation systems identify patterns or groupings in data without labels, they are unsupervised.
• Example: Content-based filtering can be seen as unsupervised when it uses product features to recommend similar items.

Clustering in Machine Learning: Supervised or Unsupervised?

A popular method for unsupervised learning is clustering. In this approach, similar data points are placed together falling into the same cluster, but no labels help here in the process of classifying.

Why Clustering is Unsupervised:

• K-Means and similar clustering algorithms work on unlabeled data.
• The aim is to discover natural clusters in the data.

KNN Algorithm in Machine Learning: Supervised or Unsupervised?

The K Nearest Neighbors (KNN) Algorithm is a supervised learning algorithm. It operates as a supervised learning algorithm for classification and regression through which the model predicts the class of the data point based on the majority class of its nearest neighbors.

Why KNN is Supervised:

• KNN requires labeled data to make predictions.
• It uses the labeled data during the prediction process to compare new data with existing labeled points.

Ensemble Learning in Machine Learning: Supervised or Unsupervised?

The name of ensemble learning is to combine the predictions of several models in order to achieve better performance. It is typically supervised because the individual models in an ensemble are usually trained with labeled data.

Why Ensemble Learning is Supervised:

• Models in ensemble learning (like Random Forest or AdaBoost) are trained on labeled datasets.
• The output of multiple models is combined to make predictions.

Random Forest in Machine Learning: Supervised or Unsupervised?

Random Forest is an ensemble method that constructs multiple decision trees during training and outputs the mode of their classes (for classification) or mean prediction (for regression) of individual trees.

Why Random Forest is Supervised:

• Random Forest requires labeled data for training.
• It is mainly used for classification or regression tasks.

Dimensionality Reduction in Machine Learning: Supervised or Unsupervised?

Dimensionality reduction such as Principal Component Analysis (PCA) typically operate in an unsupervised manner.

Why Dimensionality Reduction is Unsupervised:

• These methods aim to reduce the number of features in the data without using labeled data.
• They find patterns in the data’s structure to create a more efficient representation.

Machine Learning in Teachable Machine: Supervised or Unsupervised?

The most common approach (at least for Teachable Machine, which is a web app for training models) is supervised learning.

Why Teachable Machine is Supervised:

• You provide labeled data to train the model (e.g., images with labels).

• It learns a model for classifying new data with the labels provided.

Is Clustering Techniques Supervised or Unsupervised Machine Learning?

And clustering is an unsupervised learning method.

Why Clustering is Unsupervised:

• The algorithm works without labels and tries to find structure in the data.
• Examples include K-Means, DBSCAN, and hierarchical clustering.

Comparison Table: Supervised vs. Unsupervised Learning

FAQs

1. What is the main difference between supervised and unsupervised learning?

Supervised learning works with labeled data, and is generally used for classification or regression tasks.

2. Can a machine learning algorithm be both supervised and unsupervised?

Serious semi-supervised learning algorithms combine labeled and unlabeled input to gain lessons from data.

3. What is a common example of unsupervised learning?

The K-Means algorithm is an example of unsupervised — a common example of the many clusters you can have on different labels in clustering.

4. Is KNN supervised or unsupervised?

KNN is a supervised learning algorithm.

5. Why is SVM considered supervised?

Also, it is considered supervised since it needs labeled Data to classify or predict outputs.

Conclusion

The field of machine learning is wide and deep, with many techniques and algorithms, and whether a technique is supervised or unsupervised is based on what type of data it takes (supervised uses labels while unsupervised does not). Supervised learning deals with labeled data, and is typically used for classification or regression.

Being able to differentiate between supervised and unsupervised learning will assist you in selecting the best machine learning approach for your problem. Whether you’re applying an SVM, clustering or dimensionality reduction, understanding what you should be doing will help you make the right use of machine learning.