Lecture 03
Type of Machine Learning Datasets
In machine learning, datasets are a crucial component used for training, validating, and testing models. Datasets essentially comprise collections of data that are structured in a way that allows algorithms to learn patterns, relationships, and make predictions or classifications. There are various types of datasets commonly used in machine learning:
Training Dataset: This dataset is used to train the machine learning model. It consists of a set of examples used by the algorithm to learn patterns and relationships between input features and output labels.
Validation Dataset: Often, a portion of the dataset separate from the training data is used for model validation. This dataset helps in tuning hyperparameters and evaluating the model's performance during training to prevent overfitting.
Test Dataset: This dataset is used to evaluate the model's performance after it has been trained and validated. It contains data that the model has not seen before and is used to assess how well the model generalizes to new, unseen examples.
Labeled Dataset: In a labeled dataset, each example contains input features along with corresponding output labels. This type of dataset is used for supervised learning tasks, where the model learns to map input features to specific outputs.
Unlabeled Dataset: This dataset contains input features without corresponding output labels. Unlabeled datasets are used in unsupervised learning tasks, where the model tries to find patterns or structure within the data without explicit guidance.
Time-Series Dataset: Time-series datasets contain data points collected or recorded over a period of time, where each data point is associated with a timestamp. These datasets are common in various fields like finance, weather forecasting, and signal processing.
Image Dataset: Image datasets consist of a collection of images and are widely used in computer vision tasks such as image classification, object detection, and image segmentation.
Text Dataset: Text datasets contain textual data and are used in natural language processing (NLP) tasks, including sentiment analysis, text classification, language translation, and summarization.
Multi-modal Dataset: These datasets incorporate different types of data sources, such as text, images, audio, or video. Multi-modal datasets are used in tasks that require analysis and fusion of multiple types of information.
Imbalanced Dataset: In some cases, datasets may have an unequal distribution of classes or labels. Imbalanced datasets pose challenges for machine learning models, particularly in classification tasks, where one class has significantly more samples than others.
These dataset types serve as the foundation for various machine learning algorithms and techniques, providing the necessary information for models to learn and make predictions or decisions.
