Label encoding
by Alberto Boschetti, Luca Massaron, Bastiaan Sjardin, John Hearty, Prateek Joshi
Python: Real World Machine Learning
- Python: Real World Machine Learning
- Table of Contents
- Python: Real World Machine Learning
- Python: Real World Machine Learning
- Credits
- Preface
- I. Module 1
- 1. The Realm of Supervised Learning
- Introduction
- Preprocessing data using different techniques
- Label encoding
- Building a linear regressor
- Computing regression accuracy
- Achieving model persistence
- Building a ridge regressor
- Building a polynomial regressor
- Estimating housing prices
- Computing the relative importance of features
- Estimating bicycle demand distribution
- 2. Constructing a Classifier
- Introduction
- Building a simple classifier
- Building a logistic regression classifier
- Building a Naive Bayes classifier
- Splitting the dataset for training and testing
- Evaluating the accuracy using cross-validation
- Visualizing the confusion matrix
- Extracting the performance report
- Evaluating cars based on their characteristics
- Extracting validation curves
- Extracting learning curves
- Estimating the income bracket
- 3. Predictive Modeling
- 4. Clustering with Unsupervised Learning
- Introduction
- Clustering data using the k-means algorithm
- Compressing an image using vector quantization
- Building a Mean Shift clustering model
- Grouping data using agglomerative clustering
- Evaluating the performance of clustering algorithms
- Automatically estimating the number of clusters using DBSCAN algorithm
- Finding patterns in stock market data
- Building a customer segmentation model
- 5. Building Recommendation Engines
- Introduction
- Building function compositions for data processing
- Building machine learning pipelines
- Finding the nearest neighbors
- Constructing a k-nearest neighbors classifier
- Constructing a k-nearest neighbors regressor
- Computing the Euclidean distance score
- Computing the Pearson correlation score
- Finding similar users in the dataset
- Generating movie recommendations
- 6. Analyzing Text Data
- Introduction
- Preprocessing data using tokenization
- Stemming text data
- Converting text to its base form using lemmatization
- Dividing text using chunking
- Building a bag-of-words model
- Building a text classifier
- Identifying the gender
- Analyzing the sentiment of a sentence
- Identifying patterns in text using topic modeling
- 7. Speech Recognition
- 8. Dissecting Time Series and Sequential Data
- Introduction
- Transforming data into the time series format
- Slicing time series data
- Operating on time series data
- Extracting statistics from time series data
- Building Hidden Markov Models for sequential data
- Building Conditional Random Fields for sequential text data
- Analyzing stock market data using Hidden Markov Models
- 9. Image Content Analysis
- Introduction
- Operating on images using OpenCV-Python
- Detecting edges
- Histogram equalization
- Detecting corners
- Detecting SIFT feature points
- Building a Star feature detector
- Creating features using visual codebook and vector quantization
- Training an image classifier using Extremely Random Forests
- Building an object recognizer
- 10. Biometric Face Recognition
- Introduction
- Capturing and processing video from a webcam
- Building a face detector using Haar cascades
- Building eye and nose detectors
- Performing Principal Components Analysis
- Performing Kernel Principal Components Analysis
- Performing blind source separation
- Building a face recognizer using Local Binary Patterns Histogram
- 11. Deep Neural Networks
- Introduction
- Building a perceptron
- Building a single layer neural network
- Building a deep neural network
- Creating a vector quantizer
- Building a recurrent neural network for sequential data analysis
- Visualizing the characters in an optical character recognition database
- Building an optical character recognizer using neural networks
- 12. Visualizing Data
- 1. The Realm of Supervised Learning
- II. Module 2
- 1. Unsupervised Machine Learning
- 2. Deep Belief Networks
- 3. Stacked Denoising Autoencoders
- 4. Convolutional Neural Networks
- 5. Semi-Supervised Learning
- 6. Text Feature Engineering
- 7. Feature Engineering Part II
- 8. Ensemble Methods
- 9. Additional Python Machine Learning Tools
- A. Chapter Code Requirements
- III. Module 3
- 1. First Steps to Scalability
- 2. Scalable Learning in Scikit-learn
- 3. Fast SVM Implementations
- 4. Neural Networks and Deep Learning
- The neural network architecture
- Neural networks and regularization
- Neural networks and hyperparameter optimization
- Neural networks and decision boundaries
- Deep learning at scale with H2O
- Deep learning and unsupervised pretraining
- Deep learning with theanets
- Autoencoders and unsupervised learning
- Summary
- 5. Deep Learning with TensorFlow
- 6. Classification and Regression Trees at Scale
- 7. Unsupervised Learning at Scale
- 8. Distributed Environments – Hadoop and Spark
- 9. Practical Machine Learning with Spark
- A. Introduction to GPUs and Theano
- A. Bibliography
- Index
In supervised learning, we usually deal with a variety of labels. These can be in the form of numbers or words. If they are numbers, then the algorithm can use them directly. However, a lot of times, labels need to be in human readable form. So, people usually label the training data with words. Label encoding refers to transforming the word labels into numerical form so that the algorithms can understand how to operate on them. Let's take a look at how to do this.
- Create a new Python file, and import the preprocessing package:
from sklearn import preprocessing
- This package contains various functions that are needed for data preprocessing. Let's define the label encoder, as follows:
label_encoder = preprocessing.LabelEncoder()
- The
label_encoderobject knows how to understand word labels. Let's create some labels:input_classes = ['audi', 'ford', 'audi', 'toyota', 'ford', 'bmw']
- We are now ready to encode these labels:
label_encoder.fit(input_classes) print " Class mapping:" for i, item in enumerate(label_encoder.classes_): print item, '-->', i - Run the code, and you will see the following output on your Terminal:
Class mapping: audi --> 0 bmw --> 1 ford --> 2 toyota --> 3
- As shown in the preceding output, the words have been transformed into 0-indexed numbers. Now, when you encounter a set of labels, you can simply transform them, as follows:
labels = ['toyota', 'ford', 'audi'] encoded_labels = label_encoder.transform(labels) print " Labels =", labels print "Encoded labels =", list(encoded_labels)
Here is the output that you'll see on your Terminal:
Labels = ['toyota', 'ford', 'audi'] Encoded labels = [3, 2, 0]
- This is way easier than manually maintaining mapping between words and numbers. You can check the correctness by transforming numbers back to word labels:
encoded_labels = [2, 1, 0, 3, 1] decoded_labels = label_encoder.inverse_transform(encoded_labels) print " Encoded labels =", encoded_labels print "Decoded labels =", list(decoded_labels)
Here is the output:
Encoded labels = [2, 1, 0, 3, 1] Decoded labels = ['ford', 'bmw', 'audi', 'toyota', 'bmw']
As you can see, the mapping is preserved perfectly.
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