Training Data
- (Hong Kong)
- Overview
A machine learning (ML) model undergoes training on a dataset to learn patterns and relationships within the data. That is the fundamental process of ML.
In essence, the training process is where the "learning" happens in ML, and the dataset is the crucial input that enables this learning to occur.
ML Model Training:
- Goal: The primary goal of training a ML model is to enable it to learn patterns and relationships within a dataset so that it can make accurate predictions or decisions on new, unseen data.
- Process: This is achieved by feeding a ML algorithm a training dataset, which essentially teaches the model what to look for and how to interpret the data.
- Learning through Data: The model iteratively adjusts its internal parameters (e.g., weights and biases in algorithms) based on the training data to minimize the difference between its predictions and the desired outcomes (if provided, as in supervised learning).
- Pattern Recognition: ML models are designed to identify and recognize patterns in data, whether those patterns are explicit (like in labeled data for supervised learning) or implicit (like in unlabeled data for unsupervised learning).
- Generalization: After training, the model should be able to generalize its learning to new data that it hasn't encountered before.
Importance of the Dataset:
- Foundation of Learning: The quality and quantity of the training data are crucial for the success of a ML model.
- Guidance and Accuracy: The dataset acts as a guide, providing the model with examples and helping it learn the accurate patterns and relationships needed for accurate predictions.
- Impact of Data Quality: Incomplete, inconsistent, or biased data can lead to flawed conclusions and inaccurate predictions.
- Ensuring Generalization: High-quality datasets with a wide range of examples help the model generalize well to new data.
- Training Data
In machine learning (ML), training data is a large dataset used to train a model or algorithm. It's used to teach prediction models how to extract features that are relevant to business goals.
Training data can include: labeled images, text documents, audio recordings, sensor data. There are several types of data labeling, including:
- Audio processing: This process refers to converting sounds into written text.
- Computer vision: The computer vision process refers to the classification and tagging of visual content, such as images, in order to group them together as a data set.
- Language processing: Language processing, also called natural language processing (NLP), refers to the process of identifying text and classifying or tagging it.
Training data is used in three main types of ML: supervised learning, unsupervised learning, semi-supervised learning.
In supervised learning, the training data must be labeled. This allows the model to learn a mapping from the label to its associated features. The more training data a model has, the better it can make predictions.
- Steps for Preparing Data for ML
Here are some steps for preparing data for ML:
- Transform all the data files into a common format
- Explore the dataset using a data preparation tool like Tableau, Python Pandas, etc.
- Clean the data using mathematical operations
- Pick feature variables from the dataset using feature selection methods
- Data Transformation
Data transformation includes dimensionality reduction, feature selection, and creation of new features. These steps help reduce data noise and improve the ML model's ability to make accurate predictions.
[More to come ...]
