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  • Writer's pictureDavid Sage

From Cleaning to Testing: The Process of Preparing Data for AI and ML

Updated: Jun 1, 2023

Machine learning and artificial intelligence have revolutionized the way we approach problems in a variety of fields, from finance to healthcare, and even the arts. However, to achieve the desired results, it is crucial to prepare appropriate datasets for training, validation, and testing. Let's explore the importance and process of preparing datasets for machine learning and AI, using the example of a large dataset of classical music performances.

The Importance of Preparing Datasets

As the saying goes, "Garbage In, Garbage Out". Preparing datasets is a critical step in building machine learning and AI models. A well-prepared dataset can improve the accuracy, generalization, and performance of the models, leading to better predictions, insights, and decisions. On the other hand, a poorly-prepared dataset can result in biased, overfitting, or underfitting models that fail to capture the underlying patterns and trends in the data.

Preparing datasets involves several steps, including data collection, cleaning, pre-processing, splitting, and augmentation, among others. Each step aims to address specific issues in the data, such as missing values, outliers, class imbalance, noise, or overfitting, and to extract subsets of the data for the three vital Machine Learning tasks of training, validation, and testing.

The Process of Preparing Datasets

Data Collection: The first step in preparing datasets is to collect relevant data from various sources, such as public databases, online repositories, or proprietary systems. In our example of classical music performances, one could collect data on the composer, performer, instrument, genre, recording date, location, and other metadata, as well as actual audio or video recordings of the performances themselves.

Data Cleaning: Once the data is collected, it is necessary to clean it by removing duplicates, inconsistencies, errors, or irrelevant information. For instance, one could remove performances with low audio quality, incomplete metadata, or inaccurate labeling. Data cleaning ensures that the data is consistent, reliable, and ready for further analysis.

Data Splitting: Once the data is cleaned and pre-processed, it is typically split into three subsets: Training, Validation, and Testing. The Training set is used to train the models, the Validation set is used to tune the hyperparameters of the models and prevent overfitting, and the Testing set is used to evaluate the performance of the models on unseen data. The splitting ratio depends on the size and complexity of the dataset, as well as the nature of the problem and the models. It's critical to keep these three data sets separate from each other. If we attempt to determine the accuracy of our AI model using data it has already encountered, we can't be certain the model isn't just displaying behavior of rote memorization, and not true pattern recognition.

Data Augmentation: In some cases, it may be beneficial to augment the data by adding or modifying samples in the training set. Data augmentation can help the models generalize better by introducing variations in the data, such as shifting, rotating, or flipping the spectrograms, or adding noise or distortions to simulate real-world conditions. Data augmentation should be done carefully to avoid introducing artificial biases or artifacts in the data.

Example of a Large Dataset of Classical Music Performances

To illustrate the process of preparing datasets for machine learning and AI, let us consider the example of a large dataset of classical music performances. Suppose we have collected data on 10,000 performances of 100 classical pieces by 50 performers, recorded in various venues and dates, and stored in different formats. The goal is to build a machine learning model that can predict the performer and piece from a given audio recording.

The first step is to clean the data by removing performances with low audio quality, incomplete metadata, or inaccurate labeling. We can also remove any duplicates in the data to ensure that our model is not biased towards any particular performance or performer. Once the data is cleaned, we can pre-process it by converting the audio recordings into spectrograms, which can be used as input to our machine learning model. We can also normalize and scale the spectrograms to ensure that they have the same range of values.

Next, we need to split the data into the Training, Validation, and Testing sets. We can use an 80-10-10 split, where 80% of the data is used for training, 10% is used for validation, and the remaining 10% is used for testing. We can randomly shuffle the data before splitting to ensure that each set has an equal distribution of performers, pieces, and venues.

To prevent overfitting, we can use data augmentation techniques such as adding noise, changing the pitch or tempo, or applying random distortions to the spectrograms in the training set. This can help our model generalize better to new data and improve its accuracy.

Once the data is split and augmented, we can train our machine learning model using a variety of algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or support vector machines (SVMs). We can use the validation set to tune the hyperparameters of our model, such as the learning rate, batch size, or number of layers, and to evaluate its performance on data that it has not seen before.

Finally, we can evaluate our model on the testing set to assess its accuracy and precision. We can also use metrics such as confusion matrices, ROC curves, or precision-recall curves to visualize the performance of our model and identify any areas needing improvement.

Preparing datasets for machine learning and AI is a crucial step in building accurate, reliable, and scalable models. By following a systematic process of data collection, cleaning, pre-processing, splitting, and augmentation, we can ensure that our models are trained on representative and diverse data and can generalize well to new data. In the case of a large dataset of classical music performances, we can use spectrograms as input, split the data into training, validation, and testing sets, and use data augmentation techniques to improve our model's accuracy.

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