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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