Types of Datasets in Machine Learning

 

Do you want to build a machine learning model? Don’t know what Dataset is? Confused about which type of dataset to be used while building the model?

(New to ML? Read our Machine Learning Introduction.)

Then let’s get started with the quick guide to machine learning datasets.

What is a Machine Learning(ML) Dataset?

Dataset as the name says its a set of data. Dataset is a collection of data that is treated as a single unit for doing analytics and predictions.

The dataset used in Machine learning problems can be a population or sample dataset. Most of the time the dataset used in machine learning is a sample dataset.

Based on the patterns identified from this dataset the model makes predictions. Once the model is trained it is tested for accuracy and we look for the model working with the test dataset.

Example: Let us consider the test scores dataset of a student.

Subject	Marked obtained	Performance Level
English	85	Good
Maths	38	Poor
Science	87	Good
Social	45	Poor
Hindi	90	Excellent

How does Dataset help Machine learning models?

When working with any Machine learning problems we need datasets. We all know that data is very crucial and plays a vital role in making important decisions.

Do you know how much data is getting generated every day? It is 1.145 trillion MB per day. Yes, you read it right. So we can’t waste this data without doing anything out of it. This is where Machine learning or artificial intelligence comes into the picture. Where a dataset is used to analyze and predict the output of the model based on the learned pattern. 

But for any ML model to work successfully, you need to provide it with a good data set. Without datasets for machine learning, the algorithm will not be able to learn and solve the problems. For example, when you do not have the right books and resources, you cannot ace the test you want to. Or the best example would be without the prescription from a doctor you can’t take any number of medicines as per your wish.

What dataset is used during the ML model building?

I guess by this time you would have got to know that the training dataset is used for model building. So the dataset collected must be in proper format before feeding it to the model.

In real-time we don’t load the dataset and feed that raw dataset for the ML model, so the model will learn and give the output. No that never happens with industries projects, we do data preprocessing before giving it to the ML models.

The dataset collected must be understandable to the machine. The dataset must be uniform so the model learns better patterns than the machine that doesn’t see data as humans do.

Preparing the dataset is important as the ML algorithm cannot work on raw or unstructured data. A uniform and well-structured dataset must be provided to ML algo always so ML projects will be successful even with the training dataset.

The Training Dataset provided will be used by ML models. The training dataset is the dataset that we feed to the machine learning algorithm to train our models. This is also called a validation dataset as it is used in model validation.

What are the best public datasets that are available for machine learning?

Let see some extensively used machine learning datasets:

• Google analytics or google sheets
• Amazon sales data
• Weather dataset
• Hotel billing dataset
• Mall customer dataset
• Iris dataset
• Wine quality dataset
• Parkinson dataset
• Uber pickup dataset
• Credit card fraud detection dataset
• Chatbot intents dataset
• Email spambase dataset
• Imagenet dataset
• Cityscapes dataset
• Kinetics dataset
• Photo sketching dataset
• Youtube 8M dataset
• Librispeech dataset
• Canada government open portal dataset
• Financial times market dataset

It is advised for machine learning beginners to take these datasets and build the ML model. And to follow all the machine learning life cycles in building a better and robust ML model.

Understanding of the dataset in Machine Learning:

In normal terms when we say dataset it can be an excel spreadsheet or a table in the database(Oracle, MongoDB, MySQL, etc) and these are termed structured datasets. When it comes to an unstructured dataset it can be images, videos, etc.

When I think of data, I think of rows and columns, like a database table or an Excel spreadsheet. This is a traditional structure for data and is what is common in the field of machine learning. Other data like images, videos, and text, so-called unstructured data is not considered at this time.

The dataset that is provided to any machine learning model contains 1 or more feature columns, target columns, and instances.

Feature columns are considered as the input or independent columns. The target column is the output or dependent column as the machine tries to learn the pattern and tries to match that pattern with the output variable. A single record or row in the dataset is considered an instance.

Always note that there can be a dataset given with only feature columns without the target column which is seen in unsupervised machine learning problems. Usually, the dataset contains both input and output columns which are supervised by machine learning.

The feature and target columns may have data types like numerical or categorical or ordinal values. In simple terms, data may be integer-valued, double-valued, strings, alphanumeric, date, time, etc.

Usually, any complex data types are converted to numerical or categorical forms. Let us say if you have only categorical feature columns in the given dataset we may reduce to numerical form. Depending on the problem statement the approach may change.

And the whole dataset is split to train and test datasets. Across industries normally 60% or 70% or 80% of the whole dataset is taken as a training dataset and the rest 40% or 30 % or 20 % of the whole dataset is taken as a testing dataset. Generally, taking 70% for training and 30% for testing is the ideal and best approach so that models can learn to identify better patterns.

What are the different types of Datasets used in Machine Learning?

Usually, these are the 3 datasets that are used while building any ML model:

• Training dataset

• Validation Dataset

• Testing dataset

Training Dataset

The dataset we give to the Machine learning model is considered as the training dataset. A train set is an actual dataset that is used to train the model for performing various actions. ML algo looks for hidden data patterns to identify the features from the dataset. The output of this dataset is a machine learning model that you need to use for predicting results. The training dataset is used for building the Machine learning model.

Validation Dataset

While creating the model we use a validation data set at the validation stage. This is done right after the training phase to evaluate the ML model. Validation datasets are used to adjust the hyperparameters(values set before learning the ML model) of the model. The values cannot be estimated from the dataset. So we use hyperparameters in knowing the depth of the tree, the number of leaf nodes needed for the model, etc.
It is always a good approach to set some samples of the dataset for evaluation. The validation dataset is used for analyzing the better performance of the model.

Test Dataset

The test dataset is used to understand the accuracy of the ML model. Basically in normal terms, we can say the training dataset will tell you how much your machine learning model has learned from the training dataset. This helps in knowing how the machine learning model will work in the future. 

Generally, the training dataset is not taken in the testing dataset. This is because we try to see if the model can identify the different patterns or not based on its learning from the training dataset.

In the testing phase, we get to know whether the machine learning model built will lead to overfitting or underfitting. We know how bias and variance make the ML model overfit or underfit.

That’s all you need to take care of with the machine learning dataset. I hope this tutorial helped you in understanding what the dataset is all about and the types of the dataset used extensively in machine learning.