Data sets are essential components of data science and machine learning since they serve as the foundation for building and training predictive models.
More specifically, a data set consists of a collection of data that can either be structured (e.g., in a table or spreadsheet format) or unstructured (e.g., text or data extracted from audio or visual files). Such data sets can then be fed into machine learning or statistical models in order to perform inferences or predictions.
In the next few sections, we will discuss some of the most important characteristics of data sets, including data set balance, labeling and quality.
What Is a Data Set?
Data sets are essential components of data science and machine learning since they serve as the foundation for building and training predictive models. A data set consists of a collection of data that can either be structured (e.g., in a table or spreadsheet format) or unstructured (e.g., text or data extracted from audio or visual files). Such data sets can then be fed into machine learning or statistical models in order to perform inferences or predictions.
What Is Data Set Balance?
One of the most important things to consider when working with data sets is whether they are balanced or imbalanced.
A balanced data set is one in which the number of samples for each class is roughly the same. For example, in a binary classification problem where the goal is to classify images of dogs and cats, a balanced data set would have an equal number of images of each animal.
On the other hand, an imbalanced data set is one where the number of samples for one class is significantly higher or lower than the number of samples for the other class. This discrepancy can be a problem because it can lead to models that are biased toward the class with more samples. As such, it may not perform well on samples from the other class.
A common case involving imbalanced data is fraud detection. The majority of the examples included in such data sets usually correspond to genuine transactions while few examples (sometimes even fewer than 0.01 percent) represent fraudulent transactions.
Whenever you work on projects that involve imbalanced data sets, you can consider a number of options to improve the model’s performance. Such techniques include downsampling (i.e., removing examples from the majority class) or oversampling (i.e., simulating or adding examples from the minority class). You may also perform more sophisticated sampling techniques like stratified sampling or select models that tend to perform well with imbalanced data, such as tree-based and boosting algorithms (e.g. XGBoost) that can assign a higher weight to the minority class on every iteration.
Labeled Versus Unlabeled Data Sets
We can broadly divide data sets into two categories: labeled and unlabeled.
Labeled data sets contain a set of input data (also called features or predictors) and a corresponding set of output data (also called labels or targets). These data sets are used for supervised learning tasks, such as classification and regression problems. In classification problems, the goal is to predict a categorical label (e.g., fraud or genuine) for a given input. On the other hand, regression problems, aim to predict a continuous value (e.g., a predicted stock price) for a given input.
On the other hand, unlabeled data sets contain only input data and no corresponding output data. These data sets are used for unsupervised learning tasks, such as clustering problems. In clustering problems, the goal is to group similar data points together based on their feature values. A common use case that is largely benefited by clustering approaches is customer segmentation. During clustering analysis, the algorithm discovers groups of people with some common characteristics and clusters them together.
In classification problems, labeled data sets are used to train a model to predict the class label of new data points based on their feature values. The model is then tested on a separate set of labeled data to evaluate its performance. An example binary classification problem might be the task of predicting whether an email is spam or not. Classification problems that involve more than two categorical labels are called multi-class problems. An example of this kind of problem might involve asking the machine to identify the animal type from image data.
In regression problems, labeled data sets are used to train a model to predict a continuous value for new data points based on their feature values. The model is then tested on a separate set of labeled data to evaluate its performance. For instance, when training a ML model to predict the price of a stock, we need to train it using features extracted from historical stock price movements as well as additional features that can benefit the performance of the model (such as macro-economical data). The output value of a regression will be a continuous value that corresponds to the predicted stock price.
What Is the Silhouette Score?
The silhouette score (or silhouette coefficient) is used to evaluate the success of a clustering algorithm. The score can take values between -1 and 1, where 1 indicates that clusters fall well apart from each other (which means they are clearly separated), and 1 indicates that clusters cannot be clearly distinguished.
In clustering problems, unlabelled datasets are used to group similar data points together based on their feature values. Some common applications of clustering include medical imageing and anomaly detection. The performance of the clustering algorithm is usually evaluated using metrics such as silhouette score or Davies-Bouldin index.
What Is The Davies-Bouldin Index?
The Davies-Bouldin index is another commonly used metric when it comes to evaluating the performance of clustering analysis. This score corresponds to the average similarity measure of every single cluster with the corresponding closest cluster. Therefore, clusters that are well apart and less spread out will be assigned a better score.
What Is Data Set Quality?
Another important aspect of data sets is their quality. Several factors determine the quality of a data set, including the accuracy and completeness of the data, the representativeness of the samples, and the relevance of the data to the problem at hand.
The garbage in, garbage out principle states that no good machine learning model can be built when the input data is of low quality. In other words, if the data sets used to train and test a model are of poor quality, the model will also be so. To ensure that the data sets used in a project are of high quality, it is important to perform data cleaning and preprocessing and to use appropriate data validation techniques.
What Are Some Well-Known Data Sets?
Some classic data sets used in data science and machine learning literature include the following:
Commonly Used Data Sets
- Iris data set: A set of 150 records of iris flowers, including their species and four features (sepal length and width, petal length and width). This data set is often used for classification and clustering tasks.
- Wine data set: A set of 178 records of wine samples, including the chemical properties of the wine and the cultivar. This data set is often used for classification and clustering tasks.
- Boston housing data set: A set of 506 records of housing prices in the Boston area, including information on crime rate, property tax rate, and other factors. This data set is often used for regression tasks.
- MNIST data set: A set of 70,000 handwritten digits, each with 784 features (28 by 28 pixels). This data set is often used for image classification tasks.
- Titanic data set: A set of information on the passengers of the Titanic, including whether they survived or not. This data set is often used for classification and survival analysis tasks.
Understanding Data Sets
In conclusion, data sets are a crucial element in the field of data science and machine learning. Researchers need to consider the balance and quality of data sets when building models, as well as the type of data set in use. By paying attention to these factors, researchers ensure that the models built are accurate and reliable and that the results obtained from them are meaningful and useful.
