Which Modelling Type Is Used for Labelled Data?
When it comes to working with labelled data, there are several modelling techniques that can be employed to extract meaningful insights and make accurate predictions. In this article, we will explore some of the most commonly used modelling types for labelled data and discuss their advantages and use cases.
In supervised learning, the dataset consists of input features (also known as independent variables) and their corresponding labels (dependent variables). The goal is to train a model that can learn the underlying patterns in the data and make predictions on unseen examples.
- The linear regression model assumes a linear relationship between the input features and the Target variable.
- It is widely used for regression problems where the Target variable is continuous.
- Logistic regression is commonly used for binary classification tasks where the Target variable has two possible outcomes.
- The model calculates the probability of an example belonging to one of the classes based on its input features.
In unsupervised learning, there are no predefined labels available. The goal is to discover hidden patterns or structures in the data without any prior knowledge about the outcome variable.
- K-means clustering is a popular unsupervised learning algorithm that aims to partition a given dataset into K clusters based on similarity.
- The algorithm iteratively assigns examples to clusters by minimizing the within-cluster sum of squared distances from each example to its cluster centroid.
Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to learn hierarchical representations of data.
Convolutional Neural Networks (CNNs):
- CNNs are commonly used for image classification tasks.
- These networks consist of convolutional layers, pooling layers, and fully connected layers, which allow them to capture spatial dependencies in the data.
Recurrent Neural Networks (RNNs):
- RNNs are well-suited for sequential data such as time series or natural language processing tasks.
- These networks have recurrent connections that enable them to capture temporal dependencies in the data.
In conclusion, the choice of modelling technique for labelled data depends on the specific problem at hand and the nature of the dataset. Supervised learning algorithms like linear regression and logistic regression are suitable for problems with known labels.
Unsupervised learning techniques such as k-means clustering can be used when no labels are available but patterns need to be discovered. Deep learning models like CNNs and RNNs excel in tasks involving images or sequential data.
By understanding the different modelling types and their applications, you can choose the most appropriate technique to tackle your labelled data problem effectively.