Feature Engineering: Unlocking the Power of Data for Superior Machine Learning Models

Feature engineering is the process of selecting, manipulating, and transforming raw data into features that can be used in machine learning, mostly in supervised learning. It consists of five processes: feature creation, transformations, feature extraction, exploratory data analysis and benchmarking. In this context, a 'feature' is any measurable input that can be used in a predictive model. It could be the sound of an animal, a color, or someone's voice.

This technique enables data scientists to extract the most valuable insights from data which ensures more accurate predictions and actionable insights.

Types of features

As stated above a feature is any measurable point that can be used in a predictive model. Let's go through the types of the feature engineering for machine learning-

• Numerical features:These features are continuous variables that can be measured on a scale. For example: age, weight, height and income. These features can be used directly in machine learning.

• Categorical features:These are discrete values that can be grouped into categories. They include: gender, zip-code, and color. Categorical features in machine learning typically need to be converted to numerical features before they can be used in machine learning algorithms. You can easily do this using one-hot, label, and ordinal encoding.

• Time-series features:These features are measurements that are taken over time. Time-series features include stock prices, weather data, and sensor readings. These features can be used to train machine learning models that can predict future values or identify patterns in the data.

• Text features:These are text strings that can represent words, phrases, or sentences. Examples of text features include product reviews, social media posts, and medical records. You can use text features to train machine learning models that can understand the meaning of text or classify text into different categories.

• One of the most crucial processes in the machine learning pipeline is:feature selection, which is the process of selecting the most relevant features in a dataset to facilitate model training. It enhances the model's predictive performance and robustness, making it less likely to overfit to the training data. The process is crucial as it helps to reduce overfitting, enhance model interpretability, improve accuracy, and reduce training times.

Techniques in feature engineering

Imputation

This techniques deals with the handling of Missing values/data. It is one of the issues that you will encounter as you prepare your data for cleaning and even standardization. This is mostly caused by privacy concerns, human error, and even data flow interruptions. It can be classified into two categories:

• Categorical Imputation: Missing categorical variables are usually replaced by the most commonly occurring value in other records(mode). It works with both numerical and categorical values. However, it ignores feature correlation. You can use Scikit-learn’s 'SimpleImputer' class for this imputation method. This class also works for imputation by mean and median approaches as well as shown below.

# impute Graduated and Family_Size features with most_frequent values from sklearn.impute import SimpleImputer impute_mode = SimpleImputer(strategy = 'most_frequent') impute_mode.fit(df[['Graduated', 'age']]) df[['Graduated', 'age']] = impute_mode.transform(df[['Graduated', 'age']])

• Numerical Imputation: Missing numerical values are generally replaced by the mean of the corresponding value in other records. Also called imputation by mean. This method is simple, fast, and works well with small datasets. However this method has some limitations, such as outliers in a column can skew the result mean that can impact the accuracy of the ML model. It also fails to consider feature correlation while imputing the missing values. You can use the 'fillna' function to impute the missing values in the column mean.

# Impute Work_Experience feature by its mean in our dataset df['Work_Experience'] = df['Work_Experience'].fillna(df['Work_Experience'].mean())

Encoding

This is the process of converting categorical data into numerical(continuous) data. The following are some of the techniques of feature encoding:

• Label encoding: Label encoding is a method of encoding variables or features in a dataset. It involves converting categorical variables into numerical variables.

• One-hot encoding: One-hot encoding is the process by which categorical variables are converted into a form that can be used by ML algorithms.

• Binary encoding: Binary encoding is the process of encoding data using the binary code. In binary encoding, each character is represented by a combination of 0s and 1s.

Scaling and Normalization

Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step. For example, if you have multiple independent variables like age, salary, and height; With their range as (18–100 Years), (25,000–75,000 Euros), and (1–2 Meters) respectively, feature scaling would help them all to be in the same range, for example- centered around 0 or in the range (0,1) depending on the scaling technique.

Normalization is a scaling technique in which values are shifted and rescaled so that they end up ranging between 0 and 1. It is also known as Min-Max scaling. Here, Xmax and Xmin are the maximum and the minimum values of the feature, respectively.

Binning

Binning (also called bucketing) is a feature engineering technique that groups different numerical subranges into bins or buckets. In many cases, binning turns numerical data into categorical data. For example, consider a feature named X whose lowest value is 15 and highest value is 425. Using binning, you could represent X with the following five bins:

• Bin 1: 15 to 34
• Bin 2: 35 to 117
• Bin 3: 118 to 279
• Bin 4: 280 to 392
• Bin 5: 393 to 425

Bin 1 spans the range 15 to 34, so every value of X between 15 and 34 ends up in Bin 1. A model trained on these bins will react no differently to X values of 17 and 29 since both values are in Bin 1.

Dimensionality Reduction

This is a method for representing a given dataset using a lower number of features (i.e. dimensions) while still capturing the original data’s meaningful properties.1 This amounts to removing irrelevant or redundant features, or simply noisy data, to create a model with a lower number of variables. Basically transforming high dimensional data into low dimensional data. There are two main approaches to dimensionality reduction -

• Feature Selection: Feature selection involves selecting a subset of the original features that are most relevant to the problem at hand. The goal is to reduce the dimensionality of the dataset while retaining the most important features. There are several methods for feature selection, including filter methods, wrapper methods, and embedded methods. Filter methods rank the features based on their relevance to the target variable, wrapper methods use the model performance as the criteria for selecting features, and embedded methods combine feature selection with the model training process.

• Feature Extraction: Feature extraction involves creating new features by combining or transforming the original features. The goal is to create a set of features that captures the essence of the original data in a lower-dimensional space. There are several methods for feature extraction, including principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE). PCA is a popular technique that projects the original features onto a lower-dimensional space while preserving as much of the variance as possible.

Automated Feature Engineering Tools

There are several tools that are used to automate feature engineering, let's look at some of them.

FeatureTools-This is a popular open-source Python framework for automated feature engineering. It works across multiple related tables and applies various transformations for feature generation. The entire process is carried out using a technique called “Deep Feature Synthesis” (DFS) which recursively applies transformations across entity sets to generate complex features.

Autofeat- This is a python library that provides automated feature engineering and feature selection along with models such asAutoFeatRegressorandAutoFeatClassifier. These are built with many scientific calculations and need good computational power. The following are some of the features of the library:

• 與使用 fit() 等函數的 scikit learn 模型類似， fit_transform()、predict() 和 Score()。
• 可以透過 One hot 編碼處理分類特徵。
• 包含一個特徵選擇器類，用於選擇合適的特徵。
• 特徵的物理單位可以傳遞，相關特徵將被傳遞 可以計算一下。
• 包含 Buckingham Pi 定理 – 用於無量綱計算 數量。僅用於表格資料。
• 僅用於表格資料。

AutoML- 簡單來說，自動機器學習可以定義為一個搜尋概念，使用專門的搜尋演算法來尋找 ML 管道的每個組成部分的最佳解決方案。它包括：自動化特徵工程、自動化超參數最佳化、）神經架構搜尋（NAS

特徵工程中的常見問題和最佳實踐

常見問題

• 忽略不相關的特徵：這可能會導致模型的預測性能較差，因為不相關的特徵對輸出沒有貢獻，甚至可能會增加資料雜訊。此錯誤是由於缺乏對不同資料集與目標變數之間關係的理解和分析而造成的。

想像一家企業想要使用機器學習來預測每月銷售額。他們輸入的資料包括員工人數、辦公室面積等，這些資料與銷售額無關。
修復：透過進行徹底的特徵分析來了解哪些資料變數是必要的並刪除那些不需要的變數來避免這種情況。

• 過多特徵導致的過度擬合：模型可能在訓練資料上具有完美的表現（因為它有效地「記憶」了資料），但在新的、未見過的資料上可能表現不佳。這稱為過度擬合。這種錯誤通常是由於「越多越好」的錯誤觀念造成的。在模型中添加太多特徵可能會導致巨大的複雜性，使模型更難以解釋。

考慮一個預測未來用戶成長的應用程序，該應用程式將 100 個特徵輸入到模型中，但其中大多數特徵共享重疊資訊。
修復：透過使用降維和特徵選擇等策略來解決這個問題，以最大限度地減少輸入數量，從而降低模型複雜性。

• 未對特徵進行歸一化：演算法可能會賦予規模較大的特徵權重，這可能導致預測不準確。這種錯誤常常是由於缺乏對機器學習演算法如何運作的理解而發生的。如果所有特徵都處於相似的規模，大多數演算法都會表現得更好。

想像一下，醫療保健提供者使用患者年齡和收入水平來預測某種疾病的風險，但沒有將這些具有不同尺度的特徵標準化。
修正：應用特徵縮放技術將所有變數調整為相似的比例以避免此問題。

• 忽視處理缺失值 當遇到缺失值時，模型的行為可能會無法預測，有時會導致錯誤的預測。這種陷阱通常是由於疏忽或假設缺失值的存在不會對模型產生不利影響而發生的。

例如，線上零售商使用購買歷史資料來預測客戶流失，但沒有解決購買資料缺失的情況。
修復：實施處理缺失值的策略，例如資料插補，以統計估計值取代缺失值。

最佳實踐

• 確保處理輸入特徵中缺少的資料：在項目旨在預測房價的現實情況下，並非所有資料條目都可能包含有關房屋年齡的資訊。您可以使用「平均插補」等策略來插補缺失的數據，而不是丟棄這些條目，其中使用數據集中房屋年齡的平均值。透過正確處理丟失的數據而不是僅僅丟棄它，模型將有更多的數據可供學習，這可能會帶來更好的模型性能。

• 對分類資料使用one-hot 編碼：例如，如果我們在關於汽車的資料集中有一個特徵“顏色”，可能的值為“紅色”、“藍色”和“綠色”，我們將對其進行轉換分成三個獨立的二元特徵：「is_red」、「is_blue」和「is_green」。該策略允許模型正確解釋分類數據，提高模型的發現和預測的品質。

• 考慮特徵縮放：作為一個真實的例子，用於預測疾病的資料集可能包含年齡（1100）和血糖值測量值（70180）。縮放將這兩個特徵放在相同的尺度上，允許每個特徵對距離計算做出同等的貢獻，就像 K 最近鄰 (KNN) 演算法一樣。特徵縮放可以提高許多機器學習演算法的效能，使它們更有效率並減少計算時間。

• 創建相關的交互功能：一個例子可以包括預測房價交互，這可能是有益的。創建一個將浴室數量乘以總平方英尺的新特徵可能會為模型提供有價值的新資訊。交互功能可以捕捉線性模型無法看到的資料模式，從而有可能提高模型效能。

• 刪除不相關的特徵：在我們需要預測智慧型手機價格的問題中，智慧型手機的顏色可能對預測影響不大，可以刪除。刪除不相關的特徵可以簡化模型，使其更快、更易於解釋，並降低過度擬合的風險。

特徵工程不僅僅是機器學習中的預處理步驟；這是決定模型成功或失敗的基本面向。精心設計的特徵可以帶來更準確的預測和更好的泛化。數據表示：特徵是機器學習演算法運作的基礎。透過有效地表示數據，特徵工程使演算法能夠識別有意義的模式。因此，有抱負、甚至經驗豐富的資料科學家、機器學習愛好者和工程師必須認識到特徵工程在從資料中提取有意義的見解方面所發揮的關鍵作用。透過理解特徵工程的藝術並很好地應用它，人們可以釋放機器學習演算法的真正潛力，並在各個領域推動有影響力的解決方案。

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