In 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 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 MoreUsing semi-supervised learning to label regression data In this section, we are going to use semi-supervised learning to label the regression data. Semi-supervised learning is a type of machine learning …
Pseudo-labeling – Labeling Data for Regression-1 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 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