How do you categorize variables in machine learning feature engineering?

In machine learning feature engineering, numerical variables are categorized as continuous values that can be measured on a scale. The goal of feature engineering for numerical variables is to find a better way of representing the numeric variable in the model, where "better" connotes greater validity, better predictive power, and improved interpretation.Some common techniques for feature engineering of numerical variables include scaling, normalization, logarithmic transformation, trimming, capping, winsorizing, polynomial features, splines, categorization (bucketing or binning), interactions, and nonlinear models

Numerical variables broadly fall into discrete and continuous type. However there are variants like, positive only, with limits etc. for example age is positive only numerical. When machine learning model dealing with prediction appropriate clipping / transformers might help in discarding error optimizations for invalid values. Other interesting data type is lat long, though they look like continuous nemerical, representing so will be less-effective for obvious reasons. Similarly representing categorical value as discrete numerical value will add unwanted relationship between classes of values. For example Low,Medium,High represented as 0,1,2 can also unintentionally introduce relations like High is twice the Medium.

When dealing with numerical variables, it's also important to identify and address outliers, as they can significantly impact model performance. Techniques like log transformation or applying percentiles can help in managing skewed distributions. For continuous variables, creating bins or categories (a process known as discretization) can sometimes enhance model performance, especially in algorithms sensitive to variable scales. In supervised learning, understanding the correlation between numerical variables and the target variable is crucial for feature selection. Utilizing scatter plots, correlation matrices, or advanced statistical tests helps in identifying the most relevant features for the model.

Numeric tabular data was very common in the past. Many Kaggle playground competitions do still involve modeling tabular data with numeric columns but is besides that is way less prevalent today relatively speaking.

In machine learning, numerical variables like age, height, and income are categorized into discrete (e.g., number of children) or continuous (e.g., height) types. These variables undergo transformations, including scaling, normalization, and binning, to enhance their utility in models. Scaling ensures comparability, normalization facilitates fair comparisons, and transformations like log transformations optimize variable representation. Binning groups continuous variables into intervals for effective pattern capture. Careful handling of numerical variables is crucial for accurate machine learning models.

Some tips for working with this type of data is to check if there are too many unique categories, which can cause overfitting. A solution would be to collapse infrequent categories into "Other".

In qualitative analysis, categorical variables are classified as either nominal or ordinal. Nominal variables, such as nationality or hair color, lack an intrinsic order, while ordinal variables, like education levels or satisfaction ratings, inherently possess a hierarchy. Effective management of these variables is crucial in machine learning. Techniques like encoding, particularly one-hot or ordinal, are essential for converting categories into machine-readable formats.

Categorical variables are discrete, non-numeric data attributes that are often transformed into numerical values or representations for machine learning models There are several techniques for feature engineering with categorical variables, which can be broadly categorized into the following: 1)One-hot encoding: This technique converts categorical variables into numerical values by creating a separate column for each unique category. 2)Binary encoding: Also known as dummy variable encoding, this method converts categorical variables into numerical values by assigning a binary value (0 or 1) to each category. 3)Categorical-column encoding 4)Label encoding 5)Embedding and bag-of-words models

In managing categorical variables, encoding is key. Nominal variables are often transformed using one-hot encoding, creating binary columns for each category. For ordinal variables, label or ordinal encoding is used, assigning numerical labels that preserve their order. Choosing the correct encoding method is crucial, depending on the algorithm and data nature. For low-frequency categories, grouping sparse classes into a single category can be effective. Additionally, creating interaction features from categorical variables can capture complex relationships, aiding in enhancing model performance.

When converting a field in categorical feature it is essential to choose low cardinality values. One example would be time of the day in certain case there is an option to choose minute of the day but we can also divide day into time slots like morning, evening, night etc to have better correlation.

In the context of machine learning, text variables are a type of feature that represents text data, such as words, phrases, or sentences. Feature engineering for text variables involves transforming the text data into numerical vectors that can be utilized by machine learning models. This process is essential as machine learning algorithms typically require numerical input. Some common techniques for feature engineering with text variables include: 1) Bag-of-words model 2) Word embeddings 3) TF-IDF (Term Frequency-Inverse Document Frequency) By employing these techniques, text data can be effectively transformed into a suitable format for machine learning algorithms, enabling the extraction of information and patterns from the text data

For text variables, preprocessing is crucial. This includes cleaning (removing special characters, lowercasing), tokenization (breaking text into words or phrases), and removing stopwords (common words that add little meaning). Further, techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings (like Word2Vec) convert text into numerical format suitable for machine learning models. These methods help in capturing the semantic meaning of the text. In NLP tasks, understanding the context and nuances of language is key, so advanced methods like BERT or LSTM might be used for more complex analyses.

Image variables in machine learning refer to the use of image data as input for machine learning algorithms. Feature engineering with image data involves extracting meaningful features from the images that can be used to train machine learning models Some common techniques for feature engineering with image data include: 1) Cropping 2) Grayscaling 3) Selecting RGB channels 4) Intensity thresholds 5) Edge detection 6) Color filters These techniques can be used in conjunction with machine learning algorithms to analyze and classify images, such as in image recognition and image classification tasks.

In handling image variables, techniques like normalization (scaling pixel values) and edge detection are common. Convolutional Neural Networks (CNNs) are often used to extract features from images, as they can effectively recognize patterns like edges and shapes. Data augmentation, such as flipping or adding noise, helps models generalize better by providing varied examples. For deeper insights, transfer learning, where pre-trained models on large datasets are used, can be effective, especially when dealing with limited data. Processing image variables correctly is crucial in applications like facial recognition, object detection, and medical imaging, where accurate interpretation is vital.

In time series analysis, handling temporal dependencies is essential. ARIMA models are effective for univariate forecasting, while VAR suits multivariate contexts. For capturing complex time-related patterns, LSTM networks are useful. Time series cross-validation, using techniques like rolling windows, is distinct from traditional methods, ensuring temporal sequence integrity. Feature engineering often involves creating lag features, reflecting past values, and rolling statistics like moving averages to smooth fluctuations. These methods enhance the accuracy of predictions and aid in deciphering underlying trends in time series data.

The most common time series task is forecasting, which is not surprising because of its temporal and sequential aspect. Before even thinking about neural networks, I would recommend setting a baseline with traditional and more simple methods. The baseline forecast performance then constitutes a lower bound for the Machine Learning models' expected performance. Some simple baseline models are Naive Seasonal Model, Exponential Smoothing or Facebook Prophet. Also, for evaluating the model don't even think about using k-fold cross validation, as it will cause data leakage. Definitely use a train/test split or time series cross validation.

In time series feature engineering, various techniques are employed to extract meaningful information from the temporal data. Some common approaches include: 1) Creating temporal features 2) Feature extraction 3) Model-specific considerations 4) Handling non-uniformly spaced data 5) Incorporating domain knowledge These techniques are essential for preparing time series data for machine learning tasks, enabling the extraction of valuable insights and the development of accurate predictive models.

Time series variables are other variable types with additional dimension. For example image over time - video. Sensor reading (numerical) over time. Basically any snapshot data stacked together. However from storage perspective there are many encoding formats that helps us to store efficiently. Leveraging the underlying domain information such as key frames in video, differential encoding in case of sensor readings. From analysis aspect these series of data can give additional insight with respect to last observations. These include trend, repetitive patterns, unexpected outliers etc. Another key factor is how far we should lookback for patterns or comparison. This is often referred as time window, example running avg of 10 days etc

They can be classified into numeric-categorical types, like zip codes (numerical in form but categorical in function), and categorical-numerical types, such as academic grades (categorically structured but with numerical interpretation). Effective handling involves grouping zip codes into levels of regional segments to analyze geographical patterns or merging product code digits to categorize product families. For grades, mapping A-E to 1-5 can facilitate numerical analysis. This careful treatment of mixed variables, separating or combining their elements as needed, is crucial for extracting maximum insight and ensuring the integrity of the model’s predictive capabilities in diverse datasets.

Scaling Numerical Variables - Apply scaling to numerical variables to ensure a consistent scale across features, considering the requirements of machine learning algorithms. Feature Importance - Assess the importance of variables using feature importance techniques, guiding the selection of relevant features for model training. Correlation Analysis - Examine variable correlations to identify multicollinearity and optimize feature sets for improved model interpretability. Continuous Monitoring - Establish a system for continuous monitoring of variables throughout the machine learning lifecycle, adapting feature engineering strategies as needed for evolving data patterns.

Some other forms of high level variable types include audio which is sound pressure level over time (time series) but encoding different frequencies to capture only frequencies that audience is interested in. Radar typically works with burst of signal and time-series response. So its list of short time-series variables over time. Individual responses are co-related with original signal and delay in observation. Lidar information - point cloud over time and Many such domain specific measurements or observations that use multi-modal values.