Tag: Machine Learning Process

Machine Learning Workflow | Process Steps

Machine Learning

Machine Learning-

 

Before you go through this article, make sure that you have gone through the previous article on Machine Learning.

 

We have discussed-

  • Machine learning is building machines that can adapt and learn from experience.
  • Machine learning systems are not explicitly programmed.

 

In this article, we will discuss machine learning workflow.

 

Machine Learning Workflow-

 

Machine learning workflow refers to the series of stages or steps involved in the process of building a successful machine learning system.

 

The various stages involved in the machine learning workflow are-

 

 

  1. Data Collection
  2. Data Preparation
  3. Choosing Learning Algorithm
  4. Training Model
  5. Evaluating Model
  6. Predictions

 

Let us discuss each stage one by one.

 

1. Data Collection-

 

In this stage,

  • Data is collected from different sources.
  • The type of data collected depends upon the type of desired project.
  • Data may be collected from various sources such as files, databases etc.
  • The quality and quantity of gathered data directly affects the accuracy of the desired system.

 

2. Data Preparation-

 

In this stage,

  • Data preparation is done to clean the raw data.
  • Data collected from the real world is transformed to a clean dataset.
  • Raw data may contain missing values, inconsistent values, duplicate instances etc.
  • So, raw data cannot be directly used for building a model.

 

Different methods of cleaning the dataset are-

  • Ignoring the missing values
  • Removing instances having missing values from the dataset.
  • Estimating the missing values of instances using mean, median or mode.
  • Removing duplicate instances from the dataset.
  • Normalizing the data in the dataset.

 

This is the most time consuming stage in machine learning workflow.

 

3. Choosing Learning Algorithm-

 

In this stage,

  • The best performing learning algorithm is researched.
  • It depends upon the type of problem that needs to solved and the type of data we have.
  • If the problem is to classify and the data is labeled, classification algorithms are used.
  • If the problem is to perform a regression task and the data is labeled, regression algorithms are used.
  • If the problem is to create clusters and the data is unlabeled, clustering algorithms are used.

 

The following chart provides the overview of learning algorithms-

 

 

4. Training Model-

 

In this stage,

  • The model is trained to improve its ability.
  • The dataset is divided into training dataset and testing dataset.
  • The training and testing split is order of 80/20 or 70/30.
  • It also depends upon the size of the dataset.
  • Training dataset is used for training purpose.
  • Testing dataset is used for the testing purpose.
  • Training dataset is fed to the learning algorithm.
  • The learning algorithm finds a mapping between the input and the output and generates the model.

 

 

5. Evaluating Model-

 

In this stage,

  • The model is evaluated to test if the model is any good.
  • The model is evaluated using the kept-aside testing dataset.
  • It allows to test the model against data that has never been used before for training.
  • Metrics such as accuracy, precision, recall etc are used to test the performance.
  • If the model does not perform well, the model is re-built using different hyper parameters.
  • The accuracy may be further improved by tuning the hyper parameters.

 

 

6. Predictions-

 

In this stage,

  • The built system is finally used to do something useful in the real world.
  • Here, the true value of machine learning is realized.

 

To gain better understanding about Machine Learning Workflow,

Watch this Video Lecture

 

Next Article- Linear Regression

 

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