Pseudo-labeling – Labeling Data for Regression-1

Using 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 that combines both labeled and unlabeled data to improve the accuracy of a predictive model. In semi-supervised learning, a small amount of labeled data is used with a much larger amount of unlabeled data to train the model. The idea is that the unlabeled data can provide additional information about the underlying patterns in the data that can help the model to learn more effectively. By using both labeled and unlabeled data, semi-supervised learning can improve the accuracy of machine learning models, especially when labeled data is scarce or expensive to obtain.

Now, let’s look in detail at the pseudo-labeling method and how it is used for data labeling.

Pseudo-labeling

Pseudo-labeling is a technique used in semi-supervised learning where a model trained on labeled data is used to predict the labels of the unlabeled data. These predicted labels are called pseudo-labels. The model then combines the labeled and pseudo-labeled data to retrain and improve the accuracy of the model. Pseudo-labeling is a way to leverage the unlabeled data to improve the performance of the model, especially when labeled data is limited.

The pseudo-labeling process involves the following steps:

1. Train a model on labeled data: Train a supervised learning model on the labeled data using a training algorithm. The model is fitted to the training set using the provided labels.
2. Predict labels for unlabeled data: Use the trained model to predict the labels for the unlabeled data. These predicted labels are called pseudo-labels.
3. Combine labeled and pseudo-labeled data: Combine the labeled data with the pseudo-labeled data to form a new, larger training set. The pseudo-labeled data is treated as if it were labeled data.
4. Retrain the model: Retrain the model using the combined dataset. The model is updated using both the labeled and pseudo-labeled data to improve the model’s accuracy.
5. Repeat steps 2-4: Iterate the process by reusing the updated model to predict labels for new, previously unlabeled data, and combining the newly labeled data with the existing labeled data for the next round of model retraining, and the process is repeated until convergence.

Pseudo-labeling can be an effective way to leverage the large amount of unlabeled data that is typically available in many applications. By using this unlabeled data to improve the accuracy of the model, pseudo-labeling can help to improve the performance of supervised machine learning models, especially when enough labeled training data is not easily available.

Let’s use the house price dataset to predict the labels for regression:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np

Let’s load the house price dataset and then split the labeled data into the labeled_data DataFrame and unlabeled data into the unlabeled_data DataFrame, as follows:
# Load the data
data = pd.read_csv(“housing_data.csv”)
# Split the labeled data into training and testing sets
train_data, test_data, train_labels, test_labels = \
    train_test_split(labeled_data.drop(‘price’, axis=1), \
        labeled_data[‘price’], test_size=0.2)

This code snippet is used to divide the labeled data into two parts: a training set and a testing set. The training set contains the features (input data) and the corresponding labels (output data) that we will use to train our machine learning model. The testing set is a small portion of the data that we will use to evaluate the model’s performance. The train_test_split function from the sklearn.model_selection library helps us achieve this division while specifying the size of the testing set (in this case, 20% of the data). Let’s train the model using the training dataset for regression, as follows:
# Train a linear regression model on the labeled data
regressor = LinearRegression()
regressor.fit(train_data, train_labels)