Supervised Learning is still the king for Business

Welcome to the 934 new members this week! nocode.ai now has 17,761 subscribers.

In all the talk about fancy AI, there's a basic method that many businesses still use because it works well: supervised learning.

In today’s post, we will cover:

1. Modeling Types

2. What is Supervised Learning

3. How Supervised Learning Works

4. Supervised Learning Algorithms and Methods

5. Supervised Learning is A Pillar of Explainable AI

6. Examples of use cases by industry

7. What is xgboost

8. Short tutorial on xgboost

Let’s Dive In! 🤿

Modeling Types

Before diving into supervised learning, it's essential to understand the different types of modeling techniques in AI:

• Supervised Learning: Where the model is trained on labeled data, meaning it knows the input and the expected output.

• Unsupervised Learning: The model is provided with data without explicit instructions on what to do with it. It tries to learn the patterns and structures from the data.

• Reinforcement Learning: The model learns by interacting with an environment and receiving feedback in the form of rewards or penalties.

The supervised learning method offers a clear and reliable framework, allowing companies to achieve specific goals efficiently, optimize performance, and quickly see a return on investment.

What is Supervised Learning

At its core, supervised learning is an ML technique where you teach the algorithm by example. For instance, if you wanted the algorithm to recognize cats, you'd provide it with thousands of labeled pictures of cats. Over time, as the algorithm processes this data, it learns to recognize and differentiate cats from other entities. It's a systematic method of training algorithms to make predictions or decisions without human intervention.

How Supervised Learning Works

• Labeled Data: The foundation of supervised learning is data that has both input (features) and output (labels). For example, in a spam detection system, emails (input) are labeled as 'spam' or 'not spam' (output).

• Training the Model: Using algorithms, the model learns from the training data. It identifies patterns and relationships between the input and output.

• Testing and Validation: After training, the model's performance is evaluated using a separate set of data it hasn't seen before. This helps in ensuring the model doesn't just memorize the training data (overfitting) but generalizes well to new data.

• Prediction: Once validated, the model can make predictions on new, unlabeled data. For instance, given a new email, the trained spam detection model can predict whether it's spam or not.

• Feedback Loop: In real-world applications, as the model makes predictions and users interact with them, feedback can be gathered. This feedback can be used to further refine and train the model, making it more accurate over time.

Supervised Learning Algorithms

Several algorithms fall under supervised learning. The choice of algorithm often depends on the size, quality, and nature of data, the task to be performed, and the available computational resources:

• Linear Regression: This is used when the output variable is continuous, like predicting the temperature or age.

• Logistic Regression: Despite its name, logistic regression is used for binary classification tasks, like determining if an email is spam or not.

• Decision Trees and Random Forests: These are intuitive methods that split data based on feature values. While decision trees use a single tree, random forests use an ensemble of trees, making predictions more robust.

• Support Vector Machines: These are powerful for classification tasks. They work by finding the best hyperplane that separates data into classes.

• XGBoost (eXtreme Gradient Boosting): An advanced and efficient implementation of gradient boosting machines. XGBoost is renowned for its speed and performance. It can be used for both regression and classification problems and is highly versatile, handling structured and unstructured data types.

Examples of use cases by industry

Here are examples of use cases by industry using supervised learning techniques. Supervised learning continues to be one of the simplest techniques to apply, with high ROI.

1. Healthcare:

   • Disease prediction: Analyzing patient records to predict the likelihood of diseases like diabetes or heart conditions.

   • Medical image analysis: Classifying images to detect tumors, fractures, or other abnormalities.

2. Finance:

   • Credit scoring: Predicting the creditworthiness of an individual based on their financial history.

   • Fraud detection: Identifying potentially fraudulent transactions by analyzing patterns.

3. E-commerce:

   • Product recommendation: Suggesting products to users based on their browsing and purchase history.

   • Customer churn prediction: Anticipating which customers might leave the service, allowing for timely interventions.

4. Real Estate:

   • House price prediction: Estimating property values based on features like location, size, and amenities.

   • Mortgage approval: Predicting the likelihood of a borrower defaulting based on their financial profile.

5. Transportation:

   • Traffic prediction: Forecasting traffic congestion based on historical data and real-time inputs.

   • Vehicle maintenance: Predicting when parts might fail or require servicing.

6. Agriculture:

   • Crop yield prediction: Estimating the yield of a crop based on soil quality, weather patterns, and farming techniques.

   • Pest detection: Analyzing images of crops to detect and classify pests.

7. Energy:

   • Demand forecasting: Predicting electricity or gas demand based on historical usage and external factors.

   • Equipment failure prediction: Anticipating when machinery or infrastructure might fail, facilitating preventive maintenance.

I’m building a catalog of AI Use Cases by industry, you can access it here: AI Use Case Catalog

Evaluating Supervised Learning Model Performance

Evaluating the performance of supervised learning models in business starts with basic metrics. Accuracy measures the proportion of correct predictions, while precision and recall focus on the accuracy of positive predictions and the proportion of actual positives identified. The F1 Score harmonizes precision and recall, and the AUC-ROC metric gauges the model's ability to differentiate classes, aiming for values closer to 1.

For regression models, performance is assessed using Mean Absolute Error (MAE) and Mean Squared Error (MSE) which quantify prediction deviations. The Confusion Matrix offers a detailed view into prediction categories, highlighting areas of potential improvement, and Log Loss provides insights into prediction certainty.

Beyond these technical metrics, businesses must consider application-specific KPIs. It's vital to ensure that the model's decisions are interpretable, test its real-world impact, and confirm its scalability. This holistic approach ensures the model aligns with both technical benchmarks and overarching business goals.

Supervised Learning is A Pillar of Explainable AI

Supervised learning is often championed as a cornerstone for explainable AI for several reasons:

1. Transparent Mechanisms: Many supervised learning algorithms, such as decision trees or linear regression, have inherently interpretable structures. Their predictions can be traced back through a series of comprehensible steps or equations.

2. Feature Importance: Supervised learning models often provide insights into which features (or input variables) are most influential in making predictions. This allows stakeholders to understand which factors the model deems significant.

3. Consistent Framework: Since supervised learning relies on labeled data, there's a clear mapping between input data and desired output. This consistency offers a structured framework that aids in understanding model behavior.

4. Model Simplicity: While there are complex supervised models, many problems can be addressed with simpler models that are easier to interpret. Simpler models often mean clearer insights into how input data leads to predictions.

What is xgboost

XGBoost, or eXtreme Gradient Boosting, is an advanced implementation of gradient boosting machines. It is known for its speed, scalability, and performance. One of the reasons it's so popular is its ability to handle missing values, and its flexibility to be integrated with various types of data, be it structured data like CSV files or unstructured data like images and text.

Short tutorial on xgboost

Step 1: Install XGBoost:

pip install xgboost

Step 2: Data Preparation - Always start with clean, relevant data. Once that's ensured, split it into training and testing sets:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Step 3: Training the Model - XGBoost provides several parameters that can be fine-tuned for optimal performance:

import xgboost as xgb

model = xgb.XGBClassifier(objective="binary:logistic", n_estimators=10)
model.fit(X_train, y_train)

Step 4: Making Predictions:

predictions = model.predict(X_test)

Step 5: Evaluating the Model - It's crucial to gauge the model's performance to understand its accuracy and reliability:

from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy * 100:.2f}%")

Notebook tutorial: link

Wrapping up - show me your Supervised Learning implementations!

Supervised learning algorithms are very effective for many business applications because they allow you to train models to perform specific tasks very accurately based on labeled training data.

Unlike large language models (LLMs) and foundation models which are pre-trained on massive datasets, supervised learning models are tailored for your specific prediction and classification tasks. This makes them better suited for business needs like forecasting, predictive analytics, computer vision, and other applications where you care about performance in a narrow domain.

The tradeoff is that supervised models require more upfront effort to label training data, while LLMs can be used more flexibly. However, for many business use cases, the accuracy and reliability of supervised learning are preferable to the general capabilities of foundation models. The focused optimization of supervised learning for a well-defined task makes it a compelling choice for business applications.

Cheers!

Armand 😎