It can be tricky to decide which is the best machine learning algorithm for classification among the huge variety of different choices and types you have.
Still, there are machine learning classification algorithms that work better in a particular problem or situation than others.
You might need algorithms for: text classification, opinion mining and sentiment classification, spam detection, fraud detection, customer segmentation or for image classification. The right choice depends on your data sets and the goals you want to achieve.
On this page:
- List of the most popular and proven machine learning classifiers.
- How to choose the best machine learning algorithm for classification problems?
- Infographic in PDF.
1. Naive Bayes Classifier
Practically, Naive Bayes is not a single algorithm.
This is a group of very simple classification algorithms based on the so-called Bayesian theorem. They have one common trait:
Every data feature being classified is independent of all other features related to the class. Independent means that the value of one feature has no impact on the value of another feature.
Although it is a simple method, Naive Bayes can beat some much more sophisticated classification methods.
Its most common applications include spam detection and text document classification.
For example, they are used to classify something either as spam or not as spam.
Another example: it allows you to categorize different articles based on whether it is about healthy eating, politics, or even IT thematic.
They are great for checking texts to find out whether it’s positive or negative. Also, Naive Bayes is used in facial recognition software.
Naive Bayes Pros and Cons
- Simple to understand and easy to implement.
- Not sensitive to irrelevant features.
- Works great in practice.
- Needs less training data.
- Can be used for both multi-class and binary classification problems (binary means problems with two class values).
- Works with continuous and discrete data (see discrete vs continuous data).
- Accepts every feature is independent. This isn’t always the truth.
2. Decision Trees
The decision tree builds classification and regression models in the form of a tree structure. It decomposes a dataset into smaller and smaller subsets and thus builds an associated decision tree.
The key purpose of using Decision Tree is to build a training model used to predict values of target variables by learning decision rules. The rules are inferred from prior data (the training data).
Once the tree is built it is applied to each tuple in the database and leads to classification for that tuple.
- Easy to understand.
- Easy to generate rules.
- There are almost null hyper-parameters to be tuned.
- Complex Decision Tree models can be significantly simplified by its visualizations.
- Might suffer from overfitting.
- Does not easily work with non-numerical data.
- Low prediction accuracy for a dataset in comparison with other machine learning classification algorithms.
- When there are many class labels, calculations can be complex.
Dataaspirant.com has an easy to read and understand article explaining how decision tree algorithm works.
And if you have advanced skills, there is a great range of open source decision tree software tools.
3. Support Vector Machines (SVM)
Support Vector Machine is a machine learning algorithm used for both classification or regression problems.
However, its most common application is in classification problems.
It uses a hyperplane to classify data into 2 different groups. Just to recall that hyperplane is a function such as a formula for a line (e.g. y = nx + b).
SVM determine the best hyperplane that separates data into 2 classes. To put it in other words, we make classification by finding the hyperplane that distinguishes the two classes very well.
SVM is widely used for classifying text documents e.g. spam filtering, categorize news articles by topic, and etc.
- Fast algorithm.
- Effective in high dimensional spaces.
- Great accuracy.
- Power and flexibility from kernels.
- Works very well with a clear margin of separation.
- Many applications.
- Doesn’t perform well with large data sets.
- Not so simple to program.
- Doesn’t perform so well, when the data comes with more noise i.e. target classes are overlapping.
If you need a basic understanding of SVM algorithm, the post from Analyticsvidhya.com “Understanding Support Vector Machine algorithm from examples” is a great way to start.
4. Random Forest Classifier
It is one of the most popular machine learning classification algorithms out there. As the name suggests Random Forest algorithm is about creating trees in a forest and make it random.
The more trees in the forest, the more accurate the result is.
To work with this algorithm, it is a very good idea to be familiar with decision tree classifier.
The key difference between Random Forest and the decision tree algorithm lies on that in Random Forest, the method of discovering the root node and splitting the feature nodes runs randomly.
The random algorithm has a wide variety of applications. For example, it is used in the banking sector for finding the loyal and the fraud customers.
Another example: in e-commerce and marketing, the Random Forest used for identifying the possibility of a customer to like the recommended products base on the similar kinds of customers. It is also a good algorithm for image classification.
Random Forest can be used both for classification and the regression problems.
- The overfitting problem does not exist when we use the random forest for classification problems.
- Can be used for feature engineering i.e. for identifying the most important features among the all available features in the training dataset.
- Runs very well on large databases.
- Extremely flexible and have very high accuracy.
- No need for preparation of the input data.
- Requires a lot of computational resources.
- Time-consuming in comparison with other machine learning classification algorithms.
- Need to choose the number of trees.
5. KNN Algorithm
kNN, or k-Nearest Neighbors, is one of the most popular machine learning classification algorithms. It stores all of the available examples and then classifies the new ones based on similarities in distance metrics.
It belongs to instance-based and lazy learning systems.
The instance-based learning algorithms are those that model the tasks utilizing the data instances (or rows) in order to help make predictive decisions.
A lazy learning algorithm means it doesn’t do very much during the training process. It just stores the training data.
When new unlabeled data arrives, kNN works in 2 main steps:
- Looks at the k closest training data points (the k-nearest neighbors).
- Then, as it uses the k-nearest neighbors, kNN decides how the new data should be classified.
KNN is simple but powerful classification techniques widely used as text classifier. It is also one of the best anomaly detection algorithms.
Pros and Cons of KNN:
- Simple to understand and easy to implement.
- Zero to little training time.
- Works easily with multiclass data sets.
- Has good predictive power.
- Does well in practice.
- Computationally expensive testing phase.
- Can have skewed class distributions (If a particular class is very frequent during the training phase, it will tend to prevail the majority voting of the new example).
- The accuracy can be decreased when it comes to high-dimension data. The reason is that there is a little difference between the nearest and farthest neighbor.
- Needs to define a value for the parameter k.
How to choose the best machine learning algorithm for classification?
The best answer to the question “Which machine learning algorithm to use for classification?” is “It depends.”
First, it depends on your data. What is the size and nature of your data?
Second, what do you want to achieve classifying the data?
Typically, the best approach is to try and run some experiments before choosing the final algorithm. Still, there are some tips you can use to find your best machine learning classification algorithms:
- What is the nature of your data? Does it consist of categorical data or numerical data, or both? Naive Bayes Classifier works great with categorical and binomial data. Random forest also performs well with categorical data. SVM is a good technique for numerical data.
- How much speed do you need? Naive Bayes and SVM’s are fast for classifying. Decision trees can be slow if they have lots of branches. KNN is also slow and requires lots of memory.
- Do you need to handle complex classification? SVM can work with complex non-linear classification.
- Are you or your team familiar with how the classifier works? Naive Bayes and decision trees can be very easily explained. SVM can be hard to understand.
- Large or small data sets? Random Forest classifier runs very well on large databases. SVM doesn’t perform well on large datasets.
Nowadays, machine learning classification algorithms are a solid foundation for insights on customer, products or for detecting frauds and anomalies.
Some of the best examples of classification problems include text categorization, fraud detection, face detection, market segmentation and etc.
Today modern algorithms have abilities such as accuracy, general purpose, full automation and “off-the-shelf” manner.
Before choosing your best fit, take your time to understand and estimate carefully the available algorithms. A really good approach is to try and run some experiments before picking your solution.