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Feature Engineering for Machine Learning
Feature engineering is the practice of altering data in order to improve the performance of machine learning models. It is a critical component of the machine learning process because it assures the quality of features that have a significant influence on the machine learning model. Superior models are more likely to be produced by a machine learning expert who is well-versed in feature engineering. This post will go through many techniques to feature engineering on data in machine learning.
Feature Engineering Methods
There are many types of data and depending on the type of data, a feature engineering method is chosen. Below is a list of some feature engineering techniques −
1. Feature scaling
This method entails scaling the feature's values into a common range. To ensure that it has equal weight in the model, ranges might be like 0 to 1 or -1 to 1.
The following techniques for feature scaling are listed −
Min-Max scaling entails reducing the feature's values to a range between 0 and 1, as calculated by the formula: X__scaled = (X - X__min) / (X__max - X__min).
Standardization is the process of scaling the values of a feature to have a mean of 0 and a standard deviation of 1, as computed by the formula: (X - X mean) / X std = X scaled
Log transformation − This entails employing a logarithmic function to change the values of the feature, which can assist to lessen the influence of outliers and enhance data distribution.
2. Feature Extraction
It is a process of extracting new features from our older data.
Below are the different methods to extract features from data −
PCA − Full form of PCA is Principal component analysis. It is a process in which we decrease the dimensions of data by capturing important patterns and correlations in the data.
Independent component analysis (ICA) is the process of detecting separate sources of variability in data and dividing them into distinct features that encapsulate different elements of the data.
Wavelet transform − This involves analyzing the data at different scales and frequencies, and extracting new features that capture the patterns and relationships at each scale.
Fourier transform − This involves analyzing the data in the frequency domain and extracting new features that capture the frequency components of the data.
Convolutional neural networks (CNNs) − This involves using deep learning techniques to automatically extract features from high-dimensional and complex data, such as images and audio.
3. Feature Selection
If you select
This entails picking a subset of the most relevant characteristics in order to minimize data dimensionality and enhance model performance.
There are various methods for selecting features, including −
Filter techniques entail rating the characteristics based on some statistical measure, such as correlation or mutual information, and picking the features with the highest ranking.
Wrapper approaches entail employing a machine learning algorithm to assess the performance of several subsets of features and picking the subset with the greatest performance.
Embedded approaches include picking the most relevant characteristics within the machine learning algorithm's training phase, for as through regularization or decision tree-based algorithms.
Dimensionality reduction approaches entail translating the original characteristics into a lower-dimensional representation, such as principal component analysis (PCA) or singular value decomposition (SVD).
The feature selection approach used is determined by the nature of the data and the model's needs. In general, filter techniques are quicker and more efficient, but may not capture the entire complexity of the data, whereas wrapper methods and embedding methods are more accurate but can be computationally expensive.
4. One-hot encoding
Converting categorical variables into numerical features entails constructing a binary indicator variable for each category.
One hot encoding approach is used to express categorical variables into numerical data that may be fed into machine learning algorithms. Each category is represented in one hot encoding by a binary vector that is as long as the number of categories and has a value of 1 in the position that corresponds to the category and 0s in all other locations.
Because many machine learning algorithms cannot handle categorical data directly, one hot encoding is required. We may utilize categorical variables as input for algorithms by transforming them into numerical data. Because each category is represented by a binary vector of the same length, one hot encoding assures that each category is equally weighted.
5. Binning
This entails categorizing numerical data into discrete bins in order to lessen the influence of outliers and increase model resilience.
Binning can be done in a variety of methods, including −
Equal-width binning is the process of separating a range of values into bins of equal width. For instance, if we have a feature with values ranging from 0 to 100 and wish to generate 5 bins, each bin would have a 20-unit range (0-20, 21-40, 41-60, 61-80, 81-100).
Equal frequency binning involves dividing the data into bins with roughly the same number of data points in each. This method may be useful when the data distribution is skewed.
The borders of the bins are manually determined based on domain expertise or other criteria in bespoke binning.
Binding may be beneficial when the connection here between the feature and even the target variable is not linear, or when there are too many unique values for a feature to be employed efficiently in a machine-learning technique. Nevertheless, it might cause data loss and does not always enhance performance. Before using binning, it is critical to assess its influence on model performance.
6. Text Processing
Text processing is the alteration and analysis of text material, typically with the goal of extracting useful information. This might cover a wide range of tasks, from basic operations like removing punctuation or converting text to lowercase to more challenging tasks like identifying named things or classifying text based on its content.
Text processing methods that are often utilized include −
Tokenization is the process of separating a piece of text into separate words or tokens.
Stopword reduction is eliminating frequent terms that aren't beneficial for analysis, such as "the," "and," or "in."
Stemming and lemmatization are strategies for improving analysis that include reducing words to their root form (e.g., "running" becomes "ran").
Tagging parts of speech is marking each word in a document with its grammatical function, such as "noun" or "verb."
Named entity recognition is the process of identifying and classifying entities in a text such as individuals, organizations, and locations.
Sentiment analysis is the process of evaluating text in order to discover the overall sentiment or emotional tone.
Conclusion
To summarize, feature engineering is an important phase in machine learning that entails choosing, modifying, and inventing features to improve model performance. Domain expertise, inventiveness, and experimentation are required. While automated feature engineering approaches are being developed, human skill is still required to generate relevant features that capture the underlying patterns in the data.
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