Algorithms used in marketing
In the current market, various machine learning algorithms are used for different marketing tasks. Depending upon the nature of the task, the dataset, and the desired outcome, different algorithms are chosen. Lets support we want to classify if the email is spam or not. Here are some algorithms that can be used for this task:
1️⃣ Naïve Bayes (NB)
✅ Advantages
✔ Works well with text data (common for spam detection).
✔ Fast and efficient even for large datasets.
✔ Assumes feature independence, which is often reasonable in spam classification.
❌ Disadvantages
✖ Assumption of feature independence may not always hold.
✖ Can be less accurate if the dataset is complex and features are highly correlated.
🔹 Best When?
✔ If working with text data (e.g., spam filtering with word frequencies, (Term Frequency-Inverse Document Frequency) TF-IDF).
✔ If you need a fast and simple model.
2️⃣ k-Nearest Neighbors (KNN)
✅ Advantages
✔ Non-parametric, meaning it does not assume a distribution of the data.
✔ Works well with small datasets.
❌ Disadvantages
✖ Slow for large datasets (as it requires storing and searching for nearest neighbors).
✖ Sensitive to irrelevant features and requires feature scaling.
✖ Not ideal for text classification, as it doesn't generalize well with high-dimensional data.
🔹 Best When?
✔ If you have a small dataset with continuous numerical features.
✔ If you need an interpretable model (easy to understand but slow on large datasets).
3️⃣ Logistic Regression (LR)
✅ Advantages
✔ Works well for binary classification (spam or not spam).
✔ Provides probabilities, so we can interpret the confidence of predictions.
✔ Works well when features are independent and linearly separable.
❌ Disadvantages
✖ Assumes a linear relationship, which may not always be the case.
✖ Not ideal for high-dimensional text data unless used with feature selection (e.g., TF-IDF).
🔹 Best When?
✔ If features are numerical and independent.
✔ If you need interpretable results with probability scores.
4️⃣ Gradient Boosting (GB)
✅ Advantages
✔ Highly accurate and powerful for complex datasets.
✔ Handles both categorical and numerical data well.
✔ Works well even when features are correlated.
❌ Disadvantages
✖ Computationally expensive; takes longer to train.
✖ Can overfit if not properly tuned (e.g., too many trees).
🔹 Best When?
✔ If you have a large dataset and need high accuracy.
✔ If you are okay with longer training times in exchange for better performance.
📌 Popularity & Practical Use?
✅ Very popular in ML competitions (e.g., Kaggle).
✅ Used in finance, fraud detection, recommendation systems.
❌ Not the first choice for spam filtering, since Naïve Bayes is much faster and text-friendly.
5️⃣ Random Forest (RF)
✅ Advantages
✔ Handles missing values and noisy data well.
✔ Works well on large datasets and avoids overfitting.
✔ Can be used for both classification and regression tasks.
❌ Disadvantages
✖ Slower than Naïve Bayes for text classification.
✖ Can become too complex with many trees.
🔹 Best When?
✔ If you want a strong, stable model without worrying about overfitting.
✔ If your features are both categorical & numerical.
📌 Popularity & Practical Use?
✅ Very popular in industry for fraud detection, healthcare, finance.
✅ Used in spam detection, but not as commonly as Naïve Bayes.
6️⃣ Support Vector Machine (SVM)
✅ Advantages
✔ Works well in high-dimensional spaces (e.g., text classification).
✔ Can classify even when data is not linearly separable (using the kernel trick).
✔ Good for small-to-medium datasets.
❌ Disadvantages
✖ Slow for large datasets (not ideal for millions of emails).
✖ Harder to tune (choosing the right kernel & hyperparameters is tricky).
🔹 Best When?
✔ If your dataset is not too large and you need high accuracy.
✔ If you need strong performance on complex decision boundaries.
📌 Popularity & Practical Use?
✅ Used in text classification, bioinformatics, image recognition.
❌ Not as widely used for spam detection anymore, since deep learning & Naïve Bayes are faster.
7️⃣ Linear Regression (LR)
✅ Advantages
✔ Simple and easy to interpret.
✔ Fast to train and works well for continuous numerical predictions.
❌ Disadvantages
✖ Not suitable for classification (spam detection is a binary problem, not continuous).
✖ Assumes a linear relationship, which doesn’t always exist.
🔹 Best When?
✔ If you're working on predicting numerical values (e.g., sales, house prices).
✔ If the problem is not classification.
📌 Popularity & Practical Use?
✅ Very popular in finance, economics, and demand forecasting.
❌ Not used for spam detection, because it's not meant for classification.
✅ Most Commonly Used in Practice
1️⃣ Naïve Bayes → Used in Gmail, Yahoo, and traditional spam filters.
2️⃣ Logistic Regression → Simple and interpretable, used in some text models.
3️⃣ Random Forest / Gradient Boosting → Used when high accuracy is needed for large datasets.
4️⃣ Deep Learning (LSTMs, Transformers) → Modern AI-based spam filters (Gmail uses deep learning now).
❌ Less Common for Spam Detection
- KNN → Too slow for large datasets.
- SVM → Used before, but now outperformed by deep learning.
- Linear Regression → Not for classification.
🚀 Industry Trend:
🔹 In the past, Naïve Bayes was the gold standard.
🔹 Now, deep learning (BERT, Transformers) is replacing Naïve Bayes in modern spam filters.
Let's compare these algorithms in the next section on the basis of speed, accuracy, interpretability, and industry popularity.
📊 Machine Learning Algorithm Comparison Table
| Algorithm | Type | Speed | Accuracy | Interpretability | Best for Classification? | Best for Regression? | Industry Popularity |
|---|---|---|---|---|---|---|---|
| Naïve Bayes (NB) | Classification | ⭐⭐⭐⭐⭐ (Very Fast) | ⭐⭐⭐ (Good for text) | ⭐⭐⭐⭐⭐ (Highly Interpretable) | ✅ Yes, best for text classification | ❌ No | ⭐⭐⭐⭐ (Still used for spam detection) |
| k-NN (K-Nearest Neighbors) | Classification & Regression | ⭐ (Slow for large data) | ⭐⭐⭐⭐ (Good for small data) | ⭐⭐⭐ (Easy to understand) | ✅ Yes, for small datasets | ✅ Yes, for small numerical datasets | ⭐⭐ (Rarely used in real-world ML) |
| Logistic Regression (LR) | Classification | ⭐⭐⭐⭐ (Fast) | ⭐⭐⭐⭐ (Good for binary problems) | ⭐⭐⭐⭐⭐ (Highly Interpretable) | ✅ Yes, best for binary classification | ❌ No | ⭐⭐⭐⭐⭐ (Widely used in business, healthcare, finance) |
| Gradient Boosting (GB) | Both | ⭐⭐ (Slow) | ⭐⭐⭐⭐⭐ (Very High) | ⭐ (Hard to interpret) | ✅ Yes, very strong but slow | ✅ Yes, for complex numerical data | ⭐⭐⭐⭐ (Used in finance, fraud detection, ML competitions) |
| Random Forest (RF) | Both | ⭐⭐⭐ (Medium) | ⭐⭐⭐⭐⭐ (Very High) | ⭐⭐⭐ (Moderately interpretable) | ✅ Yes, robust for many applications | ✅ Yes, can be used for regression | ⭐⭐⭐⭐⭐ (Very popular in real-world AI) |
| Support Vector Machine (SVM) | Classification | ⭐⭐ (Slow) | ⭐⭐⭐⭐ (Good for complex data) | ⭐⭐ (Hard to interpret) | ✅ Yes, for high-dimensional data | ❌ No | ⭐⭐⭐ (Used in niche cases, but replaced by deep learning) |
| Linear Regression (LR) | Regression | ⭐⭐⭐⭐⭐ (Very Fast) | ⭐⭐⭐ (Only works well for linear data) | ⭐⭐⭐⭐⭐ (Highly Interpretable) | ❌ No | ✅ Yes, best for simple numeric predictions | ⭐⭐⭐⭐⭐ (Most widely used regression model) |
** Points to remember**
- For Text-Based Classification (Spam Detection) → Naïve Bayes or Logistic Regression is best.
- For General Classification Tasks → Random Forest or Gradient Boosting provide high accuracy.
- For Regression (Predicting Numbers) → Linear Regression or Gradient Boosting is best.
- For Small Datasets → k-NN works but is slow for large datasets.
- For Complex Boundaries → SVM works but is slower than modern deep learning models.