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Introduction to Data in Machine Learning

Data is an integral part of machine learning, as it serves as the input for training and testing machine learning models. In this blog, we will delve into the basics of data in machine learning and explore its various forms, properties, and how we split it for training and testing purposes.

What is Data?

Data refers to the raw facts and figures that are collected, organized, and analyzed to extract insights and draw conclusions. In the context of machine learning, data is used to train and test models, which can then make predictions or decisions based on new inputs.

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Example to Understand Data

To understand data better, let’s consider an example. Suppose you have a collection of data that represents the height and weight of a group of individuals. This data can be represented in a table, with each row representing a person and each column representing a feature (height and weight).

Height (in cm)Weight (in kg)
17070
17575
18080
18585

In this example, the data consists of 4 rows and 2 columns, and each row represents a person with a specific height and weight.

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How we split data in Machine Learning ?

In machine learning, it is essential to split the data into two sets: a training set and a testing set. The training set is used to train the model, and the testing set is used to evaluate the model’s performance.

Training data is the part of a dataset that is used to train a machine learning model. This data provides the input and output examples that the model uses to learn and improve its performance. Training data is crucial for the model’s development, as it helps the model to understand the relationships between the input and output variables and to make accurate predictions or decisions.

Validation data is a subset of the dataset that is used to assess the model’s performance and to fine-tune its hyperparameters during the training process. Hyperparameters are initially set before the model begins learning and can have a significant impact on the model’s performance. By using validation data to evaluate and adjust the hyperparameters, we can optimize the model’s performance and prevent overfitting, which is when the model becomes too closely tied to the training data and does not generalize well to new data.

Testing data is a separate set of data that is used to evaluate the model’s performance once it is fully trained. This data provides an unbiased assessment of the model’s capabilities, as it has not been used in the training process. By feeding the testing data into the model and comparing its predictions or decisions to the actual output, we can determine how well the model has learned from the training data and how well it can handle new, unseen data.

There are several ways to split the data, such as random sampling, stratified sampling, and k-fold cross-validation. The method used depends on the type and size of the data, as well as the goals of the machine learning project.

There are several ways to split data in machine learning, including:

  1. Random sampling: This involves randomly selecting rows from the dataset to be included in the training set and the testing set. For example, we can use a random number generator to randomly select 70% of the rows for the training set and the remaining 30% for the testing set.
  2. Stratified sampling: This involves dividing the data into groups (strata) based on certain characteristics, such as class labels or features, and then sampling from each group to create the training and testing sets. This method is useful when the data is imbalanced, i.e., when one class is significantly larger than the others.
  3. K-fold cross-validation: This involves dividing the data into k folds, where each fold is used as the testing set and the remaining folds are used as the training set. This method is repeated k times, with a different fold being used as the testing set each time. The model’s performance is then averaged over the k iterations. K-fold cross-validation is useful when the data is limited and we want to make the most of it.

The method used to split the data depends on the size and nature of the data, as well as the goals of the machine learning project. It is important to carefully consider the method of data splitting to ensure that the model is adequately trained and tested and that the results are reliable and representative of the real-world performance of the model.

Example for Splitting Data

Let’s say we want to split the data in the table above into a training set and a testing set. One way to do this is through random sampling, where we randomly select rows from the data to be included in the training set and the testing set.

For example, we can use a random number generator to select two rows for the training set and two rows for the testing set:

Training set:

Height (in cm)Weight (in kg)
17575
18080

Testing set:

Height (in cm)Weight (in kg)
17070
18585

Different Forms of Data

Data can come in various forms, including numerical, categorical, and ordinal.

  • Numerical data is data that can be measured or quantified, such as height, weight, and temperature. Numerical data can be further classified into continuous and discrete data. Continuous data can take on any value within a given range, while discrete data can only take on specific, distinct values.
  • Categorical data is data that can be divided into categories or groups. Categorical data can be further classified into nominal and ordinal data. Nominal data has no inherent order, while ordinal data has an inherent order, such as “low,” “medium,” and “high.”

Properties of Data

Data has several properties that are important to consider when working with machine learning. These properties include:

  • Quality: The quality of data refers to its accuracy, completeness, and relevance.
  • Size: The size of data refers to the amount of data that is available for analysis. Large datasets can provide more accurate and reliable insights, but they may also require more computing resources to process and analyze.
  • Diversity: The diversity of data refers to the variety of data sources, formats, and types that are included in the dataset. A diverse dataset can provide a more comprehensive view of a problem or phenomenon, but it may also require more effort to process and analyze.
  • Noise: Noise refers to irrelevant, incorrect, or redundant data that can interfere with the accuracy and reliability of the insights obtained from the data.

Facts about Data

  • Data is constantly growing: The amount of data being generated is increasing exponentially, with estimates suggesting that the world’s data volume will reach 175 zettabytes by 2025.
  • Data can be structured or unstructured: Structured data refers to data that is organized into a structured format, such as a table or spreadsheet. Unstructured data refers to data that does not have a defined structure, such as text, images, and audio.
  • Data has value: Data can be a valuable asset for businesses and organizations, as it can be used to inform decision-making, optimize processes, and improve products and services.

Conclusion

In conclusion, data plays a crucial role in machine learning, as it serves as the input for training and testing models. Data can come in various forms and has several important properties that need to be considered when working with machine learning. Understanding the basics of data in machine learning is essential for success in this field.

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