Machine learning algorithms for classification with examples

Machine learning algorithm taxonomy:

What is Machine Learning?

Machine learning is the making the predictions based on the past observations. The labeled training dataset is the input to the machine learning algorithm and the outcome of it will be followed to the classification rule, it gives the predicted classification.

Classification problems Examples:

  • Text categorization (e.g., Spam filtering)
  • Machine vision (e.g., face detection)
  • Natural- language processing (e.g., Understanding the input language)

Machine Learning CheatSheet

 

Click to enlarge the image

Machine language algorithms:

  1. Decision trees
  2. Boosting
  3. Support Vector Machines (SVMs)

Decision Trees:

It is easy to understand the rules. It’s a Non-parametric statistics approach, so we do not need to worry about the parameters. The typical approach to control the tree structure by building the very large tree that perfectly fits the training data then cut it back. Strategies to cut: Grow on the training data, then find the minimum error part and cut it off. The main advantages are very fast to train and evaluate the training datasets, relatively easy to interrupt, more accuracy, good for data sets which has missing value attributes.

Boosting:

This is the machine learning algorithm for prediction. The general method for converting the rough rule to the highly accurate prediction rule. For example, assume to discover classifiers which are higher than the random value, then the given datasets for the boosting algorithm which builds up the single classifier with high exactness. The advantages of the boosting algorithm are fast and reliable, simple and easy program, state of art accuracy, there are many applications are developed based on the boosting algorithm (e.g., Face Detection)

Support Machine Vector (SVMs):

Algorithm with high accuracy, but it is complicated to program. Fortunately, there are lots of packages are available for program using SVMs. It is very useful for text classification problem. The real time application is “recognition of handwritten characters”. The disadvantage for SVMs is “time duration to run the program is high”

For more information on SVM: Click on below link

SVM algorithm, properties and machine code in python free download

Other algorithms for machine learning:

  • Bayesian
  • Clustering
  • Artificial Neural network
  • Logistic Regression
  • Ensemble algorithms

Based on the need we can choose the best classification algorithms, you can run the experiments by making a own code in “R” and “matlab”.

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