Supervised Machine Learning Algorithms Guide with Examples

Supervised machine learning algorithms are used when you want a system to make accurate predictions from labeled data. From fraud detection and price prediction to medical classification, these algorithms learn from known outcomes and apply that learning to new inputs with consistency.

The challenge starts when you try to choose one. You search for algorithms and end up with long lists filled with academic explanations. Logistic Regression, Random Forest, SVM, Neural Networks all sound capable, but it’s unclear which one fits your data or situation.

This article removes that uncertainty. Webisoft breaks supervised ML algorithms down by type, explains how they behave in practice, and helps you choose the right option based on real constraints instead of guesswork.

What is Supervised Machine Learning and the Role of Algorithms in It

Supervised machine learning is a way for systems to learn from examples that already have correct answers. You provide input data along with the expected output, and the model learns how the two are connected.  A common example is email spam filtering, where emails labeled as spam or not spam help the system recognize useful signals and apply them to new messages.

At the core of this process are supervised machine learning algorithms. These algorithms control how labeled data is processed, how errors are measured, and how predictions improve over time. 

Types of Supervised Machine Learning 

Supervised machine learning mainly falls into two types:  Classification and Regression.  Both rely on labeled data, but they solve very different problems. This quick comparison gives you idea about types of supervised machine learning: 

It means classification answers which class does this belong to, whereas, regression answers how much or how many. Understanding this distinction matters because it directly affects which algorithms you should use.

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Supervised Machine Learning Algorithms Used Only for Classification

Classification supervised learning uses supervised machine learning algorithms to assign labeled data into fixed categories, helping you solve problems like spam filtering, fraud detection, and medical diagnosis with repeatable decisions. These algorithms are:

Logistic Regression

This is a supervised classification algorithm used to predict the probability of a data point belonging to a specific class. Logistic regression is commonly applied when outcomes are binary.

How Logistic Regression Works

The algorithm combines input features into a single weighted score. That score passes through a sigmoid function, which converts it into a value between 0 and 1. This value represents the likelihood of the input belonging to the target class. During training, the model minimizes cross-entropy loss to improve probability accuracy. A threshold is then applied to decide the final category.

When to Use Logistic Regression

You use the Logistic Regression algorithm when the target variable has two possible outcomes. Some typical cases include fraud detection, spam filtering, and churn prediction. It works best when feature relationships are mostly linear.

Why Logistic Regression Is Useful

Logistic Regression is often chosen when you need fast results and clear reasoning behind each prediction. It fits well in systems where transparency and control matter. Its benefits are as follows:

• Trains quickly, even on large datasets
• Easy to explain and audit in business or regulated environments
• Produces probability scores instead of only class labels
• Works well as a strong baseline classification model
• Helps with risk-based decisions through adjustable thresholds

Key Hyperparameters to Know

The main parameter is C. It controls regularization strength and helps prevent overfitting. Lower values apply stronger regularization. Higher values allow closer fitting to training data.

Simple Example

LogisticRegression(C=1.0).fit(X_train, y_train)

Naive Bayes

Naive Bayes is a classification algorithm based on probability theory. It’s widely used for text and document classification tasks where speed and simplicity matter.

How Naive Bayes Works

Naive Bayes applies Bayes’ theorem to calculate the probability of a class given an input. It assumes that all features contribute independently to the final prediction. During training, the model learns how often features appear within each class. At prediction time, it compares probabilities and selects the class with the highest likelihood.

When to Use Naive Bayes

You use Naive Bayes when working with text-heavy data or high-dimensional inputs. Spam detection, sentiment analysis, and topic classification are common examples. It performs well even with limited training data.

Why Naive Bayes Is Useful

Naive Bayes is often chosen when you need fast and scalable classification using supervised machine learning algorithms. It works well as a baseline model and handles large vocabularies efficiently.

• Very fast training and prediction
• Performs well with small datasets
• Handles high-dimensional data with ease
• Simple probability-based decision logic

Key Hyperparameters to Know

The main parameter depends on the variant used. For Multinomial Naive Bayes, alpha controls smoothing to handle unseen features. Higher values apply more smoothing. Lower values make the model more sensitive to rare terms.

Simple Example

MultinomialNB(alpha=1.0).fit(X_train, y_train)

Linear Discriminant Analysis

This algorithm is also known as LDA. This is a method designed to separate classes by finding the directions where they differ the most. Then the algorithm uses those differences to assign new data points to the correct class.

How Linear Discriminant Analysis Works

LDA looks at how data points from different classes are distributed. Instead of treating features independently, it studies the overall spread of each class. The algorithm finds a projection that pulls class centers far apart while keeping points within the same class close together. Classification then happens in this transformed space, not the original feature space.

When to Use Linear Discriminant Analysis

LDA works well when class boundaries are clean and data follows a roughly normal distribution. You often see it used in medical diagnosis, pattern recognition, and signal classification. It is a good choice when you want both classification and dimensionality reduction.

Why Linear Discriminant Analysis Is Useful

LDA is useful when you need clear separation with fewer features. It belongs to supervised ML algorithms that prioritize structure and stability over raw flexibility.

• Separates classes using global data distribution
• Reduces dimensionality while preserving class separation
• Performs well on smaller, well-structured datasets
• Produces consistent decision boundaries

Key Hyperparameters to Know

Important settings include solver and shrinkage. Shrinkage helps when feature correlations make covariance estimates unstable. Choosing the right solver affects speed and numerical stability.

Simple Example

LinearDiscriminantAnalysis(solver=”svd”).fit(X_train, y_train)

Quadratic Discriminant Analysis

Quadratic Discriminant Analysis, or QDA, is a classification technique that allows each class to have its own statistical shape, making it suitable for problems where class patterns differ significantly.

How Quadratic Discriminant Analysis Works

QDA models each class separately, including its own covariance structure. This lets the algorithm capture differences in how features vary across classes. Because each class is treated independently, the resulting decision boundaries are curved rather than straight. Prediction is based on which class model best explains the input data.

When to Use Quadratic Discriminant Analysis

QDA is useful when class distributions are not similar and cannot be separated by straight lines. It often performs well in cases where LDA is too restrictive. You typically use it when you have enough data to estimate class-specific statistics reliably.

Why Quadratic Discriminant Analysis Is Useful

QDA offers flexibility that simpler classifiers cannot provide. It belongs to supervised machine learning algorithms that trade simplicity for expressive power.

• Adapts to class-specific feature variation
• Handles curved and complex class boundaries
• Captures patterns missed by linear classifiers
• Provides probabilistic class assignments

Key Hyperparameters to Know

QDA relies heavily on accurate covariance estimation, which makes it sensitive when data is scarce. The reg_param setting acts as a safeguard by softening extreme covariance values.

Instead of directly controlling model complexity like regularization in linear models, this parameter helps balance flexibility with numerical stability. Small adjustments here can prevent the model from fitting noise rather than the real class structure.

Simple Example

QuadraticDiscriminantAnalysis().fit(X_train, y_train)

Supervised Machine Learning Algorithms Used Only for Regression

So far, you’ve seen how classification-focused models handle labeled categories. But not all supervised problems involve choosing between classes. Some problems demand precise numeric predictions. That’s where regression-only algorithms come in, where the output is a value, not a label. Here are the supervised regression algorithms:

Linear Regression

Linear Regression is a supervised learning algorithm used to predict a numeric value by modeling the relationship between input features and a continuous outcome. It is one of the simplest and most widely used regression methods.

How Linear Regression Works

Linear Regressions fit a straight line that best represents the relationship between inputs and the target value. The algorithm adjusts feature weights so the predicted values stay as close as possible to actual values.

Training focuses on reducing the difference between predicted and real outputs using squared error. Once trained, the model estimates outcomes by applying the learned linear relationship to new data.

When to Use Linear Regression

You use Linear Regression when the target variable is numeric and changes in a predictable way. Common use cases include price estimation, revenue forecasting, and trend analysis. It works best when feature relationships are close to linear, and noise levels are manageable.

Why Linear Regression Is Useful

Linear Regression is often selected when you want clear insights into how each feature affects the outcome. It suits problems where simplicity and interpretability matter more than complex patterns.

• Easy to understand and explain to non-technical stakeholders
• Fast to train, even with large datasets
• Shows direct impact of each input feature
• Works well as a baseline regression model

Key Hyperparameters to Know

Basic Linear Regression has no tuning parameters in its standard form. Model behavior is mostly influenced by feature selection and data preprocessing rather than configuration settings.

Simple Example

LinearRegression().fit(X_train, y_train)

Ridge Regression

This supervised ML algorithm addresses a common issue in linear models where correlated inputs lead to unstable or exaggerated predictions. It introduces controlled weight reduction to produce more reliable numeric estimates.

How Ridge Regression Works

Ridge Regression still models the relationship between inputs and outputs using a straight line. The difference lies in how the model treats feature weights during training. Instead of allowing coefficients to grow freely, the algorithm penalizes large weights. This pushes the model to distribute influence more evenly across related features, reducing sensitivity to small data changes.

When to Use Ridge Regression

You use Ridge Regression when multiple input features move together and affect the same outcome. This is common in financial indicators, economic metrics, and sensor-based data.  It is especially useful when Linear Regression fits the data but produces unstable coefficients.

Why Ridge Regression Is Useful

Ridge Regression improves prediction consistency without discarding information. It keeps all features in play while controlling how much influence each one has.

• Stabilizes models affected by multicollinearity
• Produces smoother and more reliable predictions
• Maintains all input features instead of removing them
• Performs well when many variables contribute small effects

Key Hyperparameters to Know

Ridge Regression is governed by the alpha parameter. This value determines how strongly large coefficients are reduced. A higher value enforces stronger control over weights. A lower value keeps behavior closer to standard Linear Regression.

Simple Example

Ridge(alpha=1.0).fit(X_train, y_train)

Lasso Regression

Lasso Regression focuses on simplifying regression models by actively reducing unnecessary features. It does this while still predicting numeric outcomes from labeled data.

How Lasso Regression Works

Lasso Regression builds on a linear model but applies a constraint that can shrink some feature weights all the way to zero. During training, the algorithm penalizes large coefficients in a way that encourages sparsity. This behavior forces the model to rely only on the most influential features. As a result, less important inputs are effectively ignored.

When to Use Lasso Regression

You use Lasso Regression when your dataset contains many features, but only a few truly matter. It works well in cases like feature-heavy business data, marketing attribution, or text-based numeric prediction. It is especially useful when model simplicity is a priority.

Why Lasso Regression Is Useful

Lasso Regression helps you control complexity while keeping predictions practical and interpretable.

• Automatically performs feature selection
• Reduces model complexity without manual pruning
• Improves interpretability by removing weak signals
• Works well when only a small set of features drives outcomes

Key Hyperparameters to Know

The core parameter is alpha. This value controls how aggressively feature weights are pushed toward zero. Higher values remove more features. Lower values keep more variables active in the model.

Simple Example

Lasso(alpha=0.1).fit(X_train, y_train)

Elastic Net

It’s a regression method built for situations where neither Ridge nor Lasso alone gives the right balance. It combines feature control with stability, making it useful for complex datasets.

How Elastic Net Works

Elastic Net blends two penalty styles into a single model. One part limits how large coefficients can grow, while the other pushes weak feature weights toward zero. This combination allows the model to stay stable when features are correlated, while still removing inputs that add little value. Training balances both penalties at the same time instead of choosing one behavior.

When to Use Elastic Net

You use Elastic Net when your data has many features that are related to each other. It works well in domains like finance, genomics, and marketing analytics. Elastic Net is a strong choice when Ridge keeps too many features and Lasso removes too many.

Why Elastic Net Is Useful

Elastic Net gives you flexibility without forcing a hard tradeoff between stability and simplicity.

• Handles correlated features better than Lasso
• Removes irrelevant features more effectively than Ridge
• Produces balanced and stable predictions
• Scales well to high-dimensional datasets

Key Hyperparameters to Know

Elastic Net is controlled by two values. alpha sets the overall penalty strength, while l1_ratio controls how much Lasso versus Ridge behavior is applied. Adjusting these together lets you fine-tune sparsity and stability.

Simple Example

ElasticNet(alpha=1.0, l1_ratio=0.5).fit(X_train, y_train)

Supervised Machine Learning Algorithms Used for Both Classification and Regression

Apart from the above supervised machine learning algorithms list, there are some other algorithms that can be used for both types of supervised ML. These algorithms are as follows:

Decision Trees

Decision Trees are supervised learning models that predict outcomes by repeatedly splitting labeled data to reduce uncertainty at each step, until a final class or numeric value can be assigned.

How Decision Trees Work

A Decision Tree breaks data down by asking a sequence of questions. Each split is based on a feature and a condition that best separates the data at that step. The process continues until the data reaches a final node, called a leaf. For classification, the leaf holds a class label. For regression, it holds a numeric value, often an average.

When to Use Decision Trees

You use Decision Trees when you want clear logic behind predictions. They work well with mixed data types and do not require heavy preprocessing. They are common in credit scoring, risk assessment, and rule-based decision systems.

Why Decision Trees Are Useful

Decision Trees are easy to understand and adapt to many problem types. They are often the first choice when you need interpretable results from supervised machine learning algorithms.

• Easy to visualize and explain to non-technical users
• Handles both numeric and categorical data
• Requires little data preparation
• Works for classification and regression tasks

Key Hyperparameters to Know

Tree behavior is controlled by structure-related settings. Parameters like max_depth, min_samples_split, and min_samples_leaf limit how complex the tree can grow.

Simple Example

DecisionTreeClassifier(max_depth=5).fit(X_train, y_train)

Random Forest

It’s one of the ML techniques that extends decision trees by combining many of them into a single predictive system. Instead of trusting one tree, it relies on the collective behavior of multiple trees to reach a final prediction, which helps smooth out individual errors.

How Random Forest Works

Random Forest trains a large number of decision trees using different subsets of the data and different feature combinations. Because each tree is exposed to a slightly different view of the dataset, they learn varied decision patterns. 

When a prediction is made, the model aggregates the outputs from all trees. For classification, it selects the most frequent class. For regression, it averages the predicted values. This aggregation process reduces variance and leads to more stable results.

When to Use Random Forest

Random Forest is a good choice when individual decision trees overfit or behave inconsistently. It performs well on datasets with complex feature interactions and non-linear relationships.  You often see it used in fraud detection, credit scoring, forecasting, and recommendation systems where accuracy and consistency matter more than strict interpretability.

Why Random Forest Is Useful

Random Forest is commonly selected when you need reliable performance without heavy feature engineering. It balances flexibility with stability and handles a wide range of data types and problem sizes.

• Reduces overfitting compared to single trees
• Handles non-linear relationships naturally
• Works well with high-dimensional data
• Delivers strong baseline performance with minimal tuning

Key Hyperparameters to Know

Random Forest behavior is shaped by parameters such as the number of trees, tree depth, and the number of features considered at each split.  Increasing the number of trees generally improves stability, while controlling depth helps manage overfitting. Feature selection at each split introduces randomness that strengthens generalization.

Simple Example

RandomForestClassifier(n_estimators=100,max_depth= 10).fit(X_train, y_train)

Support Vector Machines

SVMs are algorithms built around the idea of finding the most reliable boundary between data points. Instead of focusing on average behavior, they concentrate on the hardest cases near the decision edge, which makes them effective in complex classification and regression tasks.

How Support Vector Machines Work

SVM looks for a boundary that separates data points while leaving the widest possible gap between groups. This gap, known as the margin, is defined by a small number of critical data points called support vectors.

When data cannot be separated cleanly, SVM uses kernel functions to project inputs into a higher-dimensional space. For regression, the model fits a function that stays within an acceptable error range rather than predicting exact values.

When to Use Support Vector Machines

SVM is a strong choice when data has clear boundaries but complex structure. It works well with medium-sized datasets where accuracy matters more than training speed.  Common use cases include text classification, image recognition, bioinformatics, and anomaly detection. It’s also useful when the number of features is high relative to the number of data points.

Why Support Vector Machines Are Useful

This algorithm focuses on boundary precision rather than overall averages, which often leads to strong generalization. They are frequently chosen when other models struggle to separate overlapping patterns.

• Effective in high-dimensional spaces
• Handles non-linear relationships through kernels
• Resistant to overfitting when properly configured
• Works for both classification and regression problems

Key Hyperparameters to Know

SVM behavior depends heavily on parameters like C, kernel, and gamma. The C value controls how strictly the model penalizes errors, while the kernel determines how data is transformed. Gamma influences how far the influence of a single data point reaches.

Simple Example

SVC(C=1.0, kernel=”rbf”, gamma=”scale”).fit(X_train, y_train)

k-Nearest Neighbors

k-Nearest Neighbors, or k-NN, takes a very direct approach to prediction. Instead of learning a fixed model during training, it waits until a prediction is needed and then looks at the most similar data points to decide the outcome.

How k-Nearest Neighbors Works

When a new data point appears, k-NN measures its distance from all existing labeled points in the dataset. It then selects the closest neighbors based on that distance.  For supervised learning classification, the most common label among those neighbors becomes the prediction.

For regression, the predicted value is usually an average of their values.  Because there is no training phase in the traditional sense, the algorithm relies entirely on the stored data and the chosen distance metric.

When to Use k-Nearest Neighbors

k-NN works well when your dataset is relatively small and patterns are local rather than global. It is often used in recommendation systems, pattern recognition, and similarity-based search problems. You typically avoid it when datasets are very large, since prediction time grows with data size.

Why k-Nearest Neighbors Is Useful

k-NN is simple to understand and behaves intuitively. It makes decisions based on actual examples rather than abstract rules.

• No model training required
• Easy to adapt to new data
• Naturally handles both classification and regression
• Useful as a baseline for comparison

Key Hyperparameters to Know

The most important setting is k, which defines how many neighbors are considered. Smaller values make predictions sensitive to noise, while larger values smooth results. Distance metrics like Euclidean or Manhattan distance also influence behavior.

Simple Example

KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)

Neural Networks

Neural Networks are flexible models inspired by how signals pass through connected units. They are designed to learn complex patterns by stacking multiple layers that transform input data step by step. Because of this structure, they can handle both classification and regression problems.

How Neural Networks Work

A neural network processes data through layers of interconnected nodes, often called neurons. Each neuron applies a weighted transformation to its inputs and passes the result forward. As data moves through hidden layers, the network learns increasingly abstract patterns.

During training, the model compares its predictions with the correct output and adjusts weights using backpropagation. This repeated adjustment allows the network to capture relationships that simpler models cannot represent.

When to Use Neural Networks

Neural Networks are a good fit when relationships in the data are complex and non-linear. They are widely used in image recognition, speech processing, natural language tasks, and numeric prediction problems with many interacting features. You typically choose them when simpler models fail to capture the structure of the data.

Why Neural Networks Are Useful

Neural Networks offer a high degree of flexibility and modeling power. Within supervised machine learning algorithms, they are often selected when accuracy and pattern depth matter more than interpretability.

• Learns complex non-linear relationships
• Scales well with large datasets
• Supports both classification and regression tasks
• Adapts to many data types and domains

Key Hyperparameters to Know

Neural Networks are shaped by choices like the number of layers, number of neurons per layer, learning rate, and activation functions. These settings control how fast the model learns and how complex its representations become.

Simple Example

MLPClassifier(hidden_layer_sizes=(100,),learning_rate_init=0.001).fit(X_train, y_train)

Examples of Supervised Machine Learning Algorithms

Seeing supervised learning in action makes algorithm choices clearer. The examples below show how different models are used in real systems, based on whether the task involves classification, regression, or both.

Classification Examples

These problems focus on assigning inputs to predefined categories.

• Spam Detection: Logistic Regression and Naive Bayes are commonly used to filter emails by learning patterns from labeled inbox data.
• Fraud Detection: Random Forest and Support Vector Machines help identify suspicious credit card transactions by spotting abnormal behavior.
• Medical Diagnosis: Linear Discriminant Analysis and Decision Trees are used to classify diseases based on test results and patient data.

Regression Examples

These use cases involve predicting numeric values rather than labels.

• House Price Prediction: Linear Regression and Ridge Regression estimate property values using location, size, and market features.
• Sales Forecasting: Lasso Regression and Elastic Net help predict revenue while controlling feature complexity.
• Credit Scoring: k-Nearest Neighbors and Neural Networks estimate risk scores based on financial history.

Algorithms Used for Both Classification and Regression

Some problems require both decision outcomes and numeric estimates.

• Customer Churn Analysis: Gradient Boosting models predict whether a customer will leave and estimate churn probability.
• Stock Price Movement: Neural Networks are used to predict price direction along with expected movement size.

How Supervised Machine Learning Algorithms Are Trained

Training is the phase where supervised machine learning algorithms learn the relationship between inputs and known outcomes. The process is iterative, meaning the model improves gradually by learning from its own mistakes. The training process is:

Core Training Process

• Feed labeled data: The algorithm receives input features along with correct answers. These labels act as a reference point for learning.
• Make a prediction: Using its current parameters, the model generates an output based on the input data.
• Calculate loss: The prediction is compared to the true label, and the error is measured using a loss function such as cross-entropy or mean squared error.
• Update parameters: The model adjusts its internal parameters to reduce future errors. This step repeats until performance stabilizes.

This loop continues until the model reaches an acceptable level of accuracy or improvement slows.

How Training Differs by Algorithm

While the learning cycle stays consistent, each algorithm updates itself differently.

• Logistic Regression improves predictions by minimizing cross-entropy loss using gradient descent.
• Decision Trees learn by choosing feature splits that reduce impurity using metrics like Gini index or entropy.
• Neural Networks adjust weights across layers through backpropagation.
• Random Forest trains multiple decision trees independently using different data subsets.

Simple Training Loop

for epoch in range(n_epochs):     predictions = model(X_train)     loss = loss_function(predictions, y_train)     model.update_parameters(loss) This loop captures the core idea behind supervised training. The model predicts, measures error, learns, and repeats until it performs reliably on new data.

How Supervised Machine Learning Algorithms Are Evaluated

After training a model, the next step is checking how well it performs on unseen data. Evaluation helps you understand whether the algorithm has learned useful patterns or is simply memorizing examples. Here’s how the evaluation happen:

How to Choose the Right Supervised Machine Learning Algorithm for Your Situation

Choosing an algorithm is not about finding the smartest model. It’s about finding the one that fits your problem, data, and constraints without adding unnecessary complexity. You narrow the choice step by step:

Match the Problem Type First

Start by identifying what kind of output you need. This removes half the options immediately. Classification problems like spam detection or fraud detection usually progress like this: Logistic Regression → Random Forest → Neural Networks.

Regression problems like price prediction or sales forecasting usually follow this path: Linear Regression → Ridge or Lasso → Gradient Boosting You always begin simple and move toward complexity only when needed.

Choose Based on Practical Constraints

Once the problem type is clear, constraints help decide which algorithm makes sense. Here’s which one to choose:

A Simple Workflow That Works Everywhere

In real projects, you don’t test every algorithm. You follow a short loop, such as:

1. Start with a simple baseline like Logistic Regression or Random Forest
2. Test no more than three algorithms that fit your constraints
3. Measure performance using F1-score for classification or RMSE for regression
4. Pick the model that performs best on validation data, not training data

In case you are still confused about which algorithm will be the right choice for your situation, consult with the machine learning experts at Webisoft to discuss your problem.

Ready to Build Production-Grade Machine Learning Systems With Webisoft?

As an AI and machine learning development partner, Webisoft engineers end-to-end ML solutions designed for long-term performance, governance, and scale. We focus on how supervised machine learning algorithms behave in live environments, not just how they perform in controlled tests. Here’s what Webisoft delivers across machine learning for AI projects:

• Architecture design for scalable supervised learning pipelines
• Algorithm selection based on data behavior, not trends
• Model training workflows built for repeatability and monitoring
• Deployment systems that support continuous data ingestion
• Performance tuning for accuracy, latency, and cost control
• Governance-aware ML systems aligned with enterprise standards

From early model design to full production deployment, Webisoft helps you turn supervised learning into a dependable operational asset rather than a research exercise.

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Conclusion

To sum up, supervised machine learning algorithms transform labeled data into powerful prediction systems. From simple baselines to advanced models, the right choice depends on your data, constraints, and goals. 

By understanding how these algorithms work, how they are trained, and how they are evaluated, you can build systems that deliver consistent value instead of experimental results.

FAQs

Here are some frequently asked questions regarding supervised machine learning algorithms:

Which supervised algorithm is best for beginners 

Logistic Regression is best for beginners because it is easy to understand, quick to train, and clearly shows how supervised machine learning algorithms make predictions from labeled data.

Is supervised learning better than unsupervised learning 

Supervised learning is better when labeled data is available and prediction accuracy matters, while unsupervised learning is useful for discovering hidden patterns when no labeled outcomes exist.

When should I avoid supervised machine learning algorithms

You should avoid supervised machine learning algorithms when labeled data is unavailable, expensive to create, or when the goal is exploration rather than prediction or decision-making.