How to Pick a Machine Learning Algorithm? : Select the Best Model

Pick a Machine Learning Algorithm

Data is widely used in machine learning. There are various machine learning algorithms and choosing the best model for your problem is a time-consuming task.  You choose that model that has better accuracy. Even I was facing when I have a problem and wants to pick up the best Machine Learning algorithm. In this intuition of “How to”, you will learn How to pick a Machine Learning Algorithm in very easy steps.

In the end, you will definitely be able to solve your problem by selecting the best prediction model for your project.

Before further reading lets know the types and the popular machine learning algorithms. I will not go in details to explain it. If you want to know more about it then read What is machine learning?

Type of Machine Learning

There are four types a machine can learn.

  1. Supervised (Labeled Data)
  2. Unsupervised ( Unlabeled Data)
  3. Semi-Supervised (Supervised + Unsupervised)
  4. Reinforcement

Popular Machine Learning Algorithms

Here you will know the best and widely used algorithms. Explanation of each algorithm is not given.  I am assuming that you know all these algos. To more about them you can read it from here. Machine Learning

  1. Regression ( Linear and Logistic)
  2. Naive Bayes
  3. K Means Clustering
  4. K Nearest Neighbors.
  5. Decision Trees

How to Pick a Machine Learning Algorithm?

I am assuming that you have good knowledge of the Machine Learning Algorithms. Follow the below steps for finding the best algos.

Step 1: First Identify the data

In this step, you have to first lookup the dataset and determine whether data is labeled or Unlabelled.  Labeled means that data is in already is structured form. The machine didn’t have to look up for the pattern. If the datasets are unstructured and don’t have a pattern then it is Unlabelled.

Step 2:  Choose the type of Algorithms

After you have done identification of the data. If the dataset is labeled then you will choose the Supervised Machine Learning Algorithms. In the same way, you will choose the Unsupervised Machine Learning Algorithms if the data is unlabeled.

Step 3:  Select the Best Machine Learning Algorithm

When you select the type of algorithms you will not select the best algorithms according to the dataset size.

  1. If you have a larger dataset that is unlabelled, then use K Means Clustering.
  2. For the Labelled data, use regression, K- nearest neighbor (KNN), decision trees or Naive Bayes.

Step 4: Check the level of the Accuracy

In this step, you will check the accuracy of each of the machine learning algorithms. For example, for the labeled dataset, you will design all the models regression, KNN, decision trees, and Naive Bayes. You will choose the model that has a high level of accuracy. In the same way, you can do for the unlabeled dataset.

Other Methods

There are also other methods for increasing the level of accuracy.


You create different versions of the same algorithms. Like in decision trees you can create many predictors for the root node. You try to find the best score by creating different trees.

Boosting (Semi-Supervised)

In boosting you try to improve the accuracy by using more than two algorithms. For example K Mean Clustering  with the decision tree.s


It is a popular method to improve model accuracy. You first model the different machine learning algorithms and the use all the model as a stack.


How to Pick a Machine Learning Algorithm is a time consuming task for the data scientist. The identification of the dataset is a must for finding the type of machine learning. In fact, It allows you to choose algorithms that lead to a high level of accuracy.

We hope that this tutorial has helped you to clear the question regarding picking up the best machine learning algorithms. If you have any question then please contact us or message us to the data science learner official page.


Data Science Learner Team

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Meet Sukesh ( Chief Editor ), a passionate and skilled Python programmer with a deep fascination for data science, NumPy, and Pandas. His journey in the world of coding began as a curious explorer and has evolved into a seasoned data enthusiast.
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