Preprocessing is an important step in EDA, as it can help to reduce noise and improve the quality of the images. Common preprocessing techniques include resizing, normalization, data augmentation, image …
Performing data preprocessing – Exploring Image Data Read MoreThe first step in any EDA process is to load the image data into your Integrated Development Environment (IDE) workspace, such as VS Code, Jupyter Notebook, or any other Python …
Loading the data – Exploring Image Data Read MoreBy iterating through each value in noise_range and adding noise to each data point’s price feature, the code generates multiple data points with different levels of noise. This process results …
Using data augmentation to label regression data – Labeling Data for Regression-2 Read MoreData augmentation can be used to generate additional labeled data for regression tasks where labeled data is limited. Here is a way to use data augmentation to label regression data: …
Using data augmentation to label regression data – Labeling Data for Regression-1 Read MoreIn this code snippet, we’re building and training a linear regression model using the labeled data. First, we import the LinearRegression class from the sklearn.linear_model library. Then, we create an …
Pseudo-labeling – Labeling Data for Regression-2 Read MoreIn this section, we are going to generate house price labels using summary statistics of a small set of available labeled housing price data. This is useful in real-world projects …
Using summary statistics to generate housing price labels – Labeling Data for Regression Read MoreIn this chapter, we will explore the process of labeling data for regression-based machine learning tasks, such as predicting housing prices, in situations where there is insufficient labeled data available …
Technical requirements – Labeling Data for Regression Read MoreIn classification tasks, the Random Forest classifier is used to predict and assign a class or category to a given input based on a set of input features. It’s particularly …
Random Forest classifier – Labeling Data for Classification Read MoreSemi-supervised learning falls in between supervised learning and unsupervised learning: In this case, first we will generate the pseudo-labels using a small part of the labeled dataset with supervised learning: …
What is semi-supervised learning? – Labeling Data for Classification Read MoreLabeling rules The labeling rules from our small-volume income dataset are as follows: Constants Let us define various constants that are used in our labeling functions as follows. Abstain indicates …
Labeling functions – Labeling Data for Classification Read More