In this chapter, we will learn how to explore image data using various packages and libraries in Python. We will also see how to visualize images using Matplotlib and analyze …
Technical requirements – Exploring Image Data Read MoreIn summary, the line cluster_mask = (kmeans_model.labels_ == cluster_idx) creates a mask that helps you filter and select the data points in a specific cluster based on their assigned cluster …
Using k-means clustering to label regression data – Labeling Data for Regression-2 Read MoreIn this section, we are going to use the unsupervised K-means clustering method to label the regression data. We use K-means to cluster data points into groups or clusters based …
Using k-means clustering to label regression data – Labeling Data for Regression-1 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 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 MoreWe then loop through each row in the unlabeled data and compute the distances to each target label’s summary statistics using Euclidean distance. We select the target label with the …
Finding the closest labeled observation to match the label – Labeling Data for Regression Read MoreHere, pairwise_distances from scikit-learn is used to calculate the distances between cluster centers (inter-cluster distances). The result is a matrix where the element at position (i, j) represents the distance …
What is unsupervised learning? – Labeling Data for Classification-2 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 MoreL_train is passed to the label model to predict the labels, as follows:X[‘Labels’] = label_model.predict(L=L_train) We get the following result: Figure 2.4 – Probabilistic labels As we have seen, we …
Predicting labels – Labeling Data for Classification Read MoreHours per week rule function The hours per week rule function is used to check whether the working hours per week of the person is greater than 40; if so, …
Creating a label model – Labeling Data for Classification Read More