How do you use Scikit-learn for feature engineering?

It is important to understand difference in normalization and scaling and which might work best on your data. Visualizing your data and making the decision is a good way to go. Scaling changes the range of your data whereas normalization changes distribution of your data.

Scikit-learn is a powerful Python library for machine learning, and it provides various tools for feature engineering. One helpful approach is to use the FeatureUnion class. This allows you to combine multiple feature extraction methods or transformers into a single transformer, which can be a part of your machine learning pipeline. It's particularly useful when dealing with different types of data, such as text and numerical data. This way, you can engineer and preprocess features efficiently in a unified manner. Some practitioners argue that feature engineering in Scikit-learn can be limited when dealing with complex data or specific domain requirements.

Scaling and normalization are critical data preprocessing steps used to prepare data for analysis and modeling. Scaling: It adjusts data to a specific range or standard scale, making all features comparable. Common methods include Min-Max scaling (0 to 1) and Standardization (mean 0, standard deviation 1). Normalization: This process rescales data to a common unit, often for algorithms where relative values matter more than their magnitude, like text analysis. L2 normalization (Euclidean norm of 1) is one method.Both techniques enhance machine learning algorithm performance, promote faster convergence, and improve interpretability, making them vital in various data-driven fields.

Using code snippet: - 1st data is preprocessed using StandardScaler to normalize data. This is important as many algorithms are sensitive to scale of data. - 2nd, most important features are selected using a SelectKBest selector. It selects k most important features based on a chi-squared test. - 3rd ML is trained on selected features. -----Code---- import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.feature_selection import SelectKBest # Load data X = np.loadtxt('data.csv', delimiter=',') # Preprocess the data scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Select the most important features selector = SelectKBest(k=10) X_selected = selector.fit_transform(X_scaled) # Train ML model -------

Neither normalization nor standardization will change the distribution of your data. This is quite counter-intuitive for newcomers to data science, as the terminology is confusing... If your variable is not normally distributed, it won’t be after applying the normalize() method. Standardization and normalization changes all of your features to be on the same scale. If you intend to change the distribution of your data you need to apply a non-linear transformation such as a logarithmic function or square root. The scikit learn normalize() function creates a standard normal or z-distribution for normally distributed data, else it maintains the shape of whatever the distribution is but changes the mean to 0 and standard deviation to 1.

While developing machine learning systems one needs to understand data behavior and its distribution to a large extent. This is due to the fact that future predictions purely depend upon the actual value range of input variable(s). If there exist a variable/feature which is out of range , not so obvious or an outlier then predictions will be going far away from the actual value. That is why, we would need to scale and standardize data before modelling. All input data needs to be scaled and standardized as per the use case requirement. There are different types of scaling techniques used such as minmax scaling technique, z-score normalization. You can use any of the techniques to scale data in range and then use it for training process.

Working on a data science project getting data from plant sensors like temperature sensors, pressure sensors and others. One thing that increase a lot was exactly use scaling and normalization techniques. As all these quantities have different dimensions, the use of scaling mainly impacted the model's performance.

Standardisation and Normalisation preprocesses dataset so that the training results are more similar amongst other models trained with standardised and normalised datasets. Of course, if the underlying characteristics of the datasets are vastly different, you'll get greatly different trained models. At least, one can't attribute the differences to the dissimilar scales amongst datasets. Standardisation works better with features having just a lower bound; normalisation works better with features having both upper and lower bounds.

Scaling and normalisation are dependent on the model being built and the nature of the feature values. For example, tree-based models don't require any sort of feature scaling. In Normalisation, the change in values is that they are at a standard scale without distorting the differences in the values. Whereas, Standardisation assumes that the dataset is in Gaussian distribution and measures the variable at different scales, making all the variables equally contribute to the analysis

One way I've applied Scikit-learn for feature engineering professionally is by using its preprocessing tools to scale and normalize numerical features. For instance, in a predictive maintenance project for industrial machinery, we applied the StandardScaler to transform sensor data. This improved the model's performance by ensuring that each feature contributed equally to the prediction, preventing any single variable from dominating the learning process.

If you're dealing with neural networks, the best way to account for categorical features is to create embeddings. You feed one-hot encoded features, and let the Neural Network create embeddings during the training phase. It forces the model to map those combination of features into an N-dimensional space which is more representative of the combination of your categorical features. Once you've learned those embeddings, use them in you final model.

Encoding categorical variables properly is also important. OrdinalEncoder and OneHotEncoder in Scikit-learn can encode categories into numbers and dummy variables respectively. This prepares categorical data for machine learning.

Encoding required when you are model is dependent on numerical input in such case we need to transform the categorical features into numerical features. There are several ways of encoding techniques each with its own pros and cons, choose your encoding mechanism carefully to get transform your categorical data into numerical data

Beware of one-hot encoding when planning to use a model such as linear regression, where using all the new columns generated by the encoding process can cause problems due to multicollinearity. This happens because the nth one-hot column can be perfectly predicted by the remaining n-1 columns. The problem is easily tackled by dropping one column, you just need to remember to do it.

In classical ML, possessing domain knowledge is crucial. Not all categorical features are suitable for one-hot encoding. Consider "rank" or "grade" as examples. While the algorithm might still have the potential to learn, stripping the values of their inherent meaning diminishes a significant feature. Moreover, rather than generating multiple columns through one-hot encoding, you're creating just one replacement column. This helps mitigate or reduce the "curse of dimensionality" that could result from indiscriminately applying one-hot encoding to all categorical features.

At work, we utilized Scikit-learn's LabelEncoder and OneHotEncoder to handle categorical data. In a retail recommendation system, we encoded product categories and user preferences. This allowed us to incorporate categorical information into our machine learning models, making them more effective in providing personalized recommendations.

One way to encode categorical features in Scikit-learn is to use a technique called target encoding. This technique is particularly useful for ordinal features, which are categorical features with a natural ordering, such as customer satisfaction ratings or employee seniority levels. Target encoding has several advantages over other encoding techniques, such as label encoding and one-hot encoding. First, target encoding preserves the ordinal nature of the feature, which can lead to better performance for machine learning models. Second, target encoding can be used to encode features with a large number of categories without creating a large number of new features, which can reduce overfitting.

Sometimes you can also put numerical features in buckets, and use the buckets as categories. For example, an "income" feature (numerical) could be transformed into "high_income"/"medium_income"/"low_income". And those features are converted back to numbers by the process described in this section. Where you draw the limits is not an exact science and requires some experimentation. But in my experience, it can improve the model performance quite a bit. You could also do this for the target label (i.e.: if you were predicting the income in the previous example)

In my experience, Scikit-learn's SimpleImputer was invaluable in dealing with missing data. In a healthcare analytics project, we used it to estimate and fill in missing patient data, ensuring that we had complete datasets for analysis. This improved the accuracy and reliability of our predictive models.

Imputing missing values must be approached with caution. Whether you are using the mean, median, or model inference to infer the missing values, you are baking in statistical assumptions - which could lead to a "garbage in, garbage out" scenario. For example, if you use a model to infer the missing values, it may deterministically assign specific values to certain features that are stochastic in nature, or vice versa. More often than not, you would want to first consider whether you can simply drop the features or rows of the missing values, or whether you should put effort into collecting more data, before you start considering imputing values. And if you decide to impute values, it is worth to run experiments, to identify any problems.

Imputing missing values by using the mean is rather a simple and naive way. More robust approaches aims to train a small model, which takes all of the other features as input and uses the column on which we want to impute missing values as label. You just use the labeled part of your dataset. Once you've trained this model, use it to infer the missing values for the other records.

From another angle: Before undertaking any form of imputation, it's essential to investigate the cause of the missing values, where feasible. A subject matter expert should clarify if there's any significance to these absences. "Missing" doesn't equate to "meaningless", and incorrect imputation can impact scores.

Imputing missing values is a critical step in data preprocessing. Firstly, understanding the nature of missingness whether it's random or patterned—can significantly guide the imputation strategy. Choosing a imputation method depends upon the data distribution and the problem at hand. Advanced imputation methods like K-Nearest Neighbors Imputation or model-based imputation are worth considering for more accurate filling of missing values. Creating an indicator feature to mark where values were initially missing can help preserve the original data structure. Incorporating domain knowledge can provide more tailored solutions for handling missing values, thereby ensuring better data integrity and potentially improving model performance.

I have found it useful in many cases to provide an additional column with a binary flag, indicating if data in a particular row was imputed in some way. There are many flavors of doing this. For instance, you may have a flag only when the imputed values form more than a certain percentage of the total. You can also consider flagging only if a certain pair of values across two different columns was imputed.

Beyond basic polynomial features, explore the interaction_only parameter in Scikit-learn's Polynomial features. This approach unveils hidden feature relationships without expanding feature dimensions, enhancing model performance while avoiding overfitting. Normalize these features as they can have varying scales, effectively handled with Scikit-learn's StandardScaler. Employ feature selection to retain valuable polynomial features, boosting model efficiency and precision. This technique reveals latent interactions, maintaining model robustness and clarity and mitigating the curse of dimensionality.

Generating polynomial features can unveil complex relationships in data, enhancing model performance. However, it can lead to a high dimensionality and overfitting, especially with a high polynomial degree. Scikit-learn's PolynomialFeatures class is a handy tool for this, but caution should be exercised to maintain a balance between complexity and interpretability, while also considering the computational cost involved. The process, although powerful, requires a judicious approach to ensure effective modeling.

On thing I've found useful, other than these methods, is to train a ML model, print feature importances and then cut-out the least important ones. Then re-iterate the training process without those features, and you often end up with a simpler and more powerful model. In this case you let your model do the feature selection for you

For dimensionality reduction, Scikit-learn offers tools like PCA and feature selection techniques. In a fraud detection application, we employed PCA to reduce the dimensionality of transaction data, preserving the most relevant information. This streamlined our model and improved its efficiency without sacrificing accuracy.

Make sure you put every transformation into a pipeline. This is crucial to ensure that inference data will undergo the same exact transformations as the training data!

Scikit-learn, a widely-used Python library for machine learning, offers an array of tools and techniques for effective feature engineering. 1. Importing the Necessary Libraries: Code: import numpy as np import pandas as pd from sklearn.preprocessing import StandardScaler, PolynomialFeatures from sklearn.feature_selection import SelectKBest, f_regression 2. Handling Missing Data: Code: from sklearn.impute import SimpleImputer imputer = SimpleImputer(strategy='mean') X_imputed = imputer.fit_transform(X) 3. Feature Scaling: Code: scaler = StandardScaler() X_scaled = scaler.fit_transform(X) 4.Feature Selection: Code: selector = SelectKBest(score_func=f_regression, k=10) X_selected = selector.fit_transform(X, y)

Scikit-learn can also be used to detect outliers using tools like the Local Outlier Factor; unlike missing data which can sometimes be eliminated by dropping rows, outliers need to be considered for whether they represent a valid alternate "mode of operation" that needs its own models.

When using Scikit-learn for feature engineering, remember to assess the impact of your choices on model performance. Conduct thorough cross-validation to validate the effectiveness of your feature engineering techniques. Additionally, be mindful of potential data leakage when performing feature engineering, as unintentional information from the test set can be incorporated into the training process.

Feature engineering is super powerful when creating models (especially for tabular data), but it can also lead to leakage - maybe you're adding a feature for your training set that you wouldn't know at inference time (for aggregated features in time series data, etc.). Another thing I've encountered is that features don't introduce leakage but still are not available at inference time. Maybe you have a model deployed via a REST API for a real-time use case and we cannot create these features "on the fly". In practice, this is where features stores can help but there aren't always guaranteed solutions to all problems, and it pays off to think about the production conditions when feature engineering.