These 10 scikit-learn patterns cover variance filters, supervised ranking, model-based selection and recursive elimination. They are not 10 interchangeable algorithms: choose one that fits your target and feature assumptions, then evaluate it inside a pipeline so selection does not leak information from validation data.
Set up the examples
Each snippet assumes X contains the feature columns and y contains the target. The snippets show the relevant scikit-learn imports beside each pattern; install scikit-learn and ensure your data is in a format accepted by the selected estimator and score function. For supervised selectors, fit only on training data or put the selector in a pipeline as shown below.
10 feature-selection one-liners
1. Remove constant features
VarianceThreshold is unsupervised: it looks at X, not y. Its default threshold of zero removes features that do not vary.
from sklearn.feature_selection import VarianceThreshold
X_var = VarianceThreshold().fit_transform(X)2. Remove features below a variance floor
A positive threshold removes low-variance features as well. The example uses 0.01 only as an illustration; variance depends on feature scale, so choose a threshold appropriate to your data rather than treating this value as universal.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
#1 Best Overall
- Read Before You Buy — No Video Output: These adapters support charging and USB 2.0 data transfer, but cannot transmit video signals. Except for standard USB webcams (which use USB data only), they are not compatible with HDMI/DisplayPort cables, video-capable USB-C hubs, or docking stations with video output.
- Convert USB-A Ports to USB-C: Designed to connect USB-C earphones, cables, flash drives, card readers, and other USB-C accessories to standard USB-A ports. Plug-and-play with no drivers or software required.
- Aluminum Alloy Housing: Built with a sturdy aluminum alloy shell that aids in heat dissipation and protects against daily wear and scratches. Designed to maintain a stable and secure connection.
- Compact & Travel-Friendly: The ultra-compact design allows the adapter to stay plugged into your device without blocking adjacent ports or adding bulk, reducing wear and tear on your original USB ports.
- 12-Month Warranty: Backed by a 12-month manufacturer warranty for peace of mind. Designed to meet strict quality control standards for reliable everyday performance.
from sklearn.feature_selection import VarianceThreshold
X_var = VarianceThreshold(threshold=0.01).fit_transform(X)3. Keep the top features by ANOVA F-score for classification
f_classif scores each feature individually against a classification target. SelectKBest retains the requested number of highest-scoring features.
from sklearn.feature_selection import SelectKBest, f_classif
X_top = SelectKBest(f_classif, k=10).fit_transform(X, y)4. Keep the top features by F-score for regression
For a regression target, use f_regression instead of the classification score.
Rank #2
- 5-in-1 USB-C Hub: Experience comprehensive connectivity featuring a Power Delivery input, two USB-A 2.0 ports, a USB-A 3.0 port, and an HDMI port. (Note: The USB-C power delivery input port is only for connecting an external wall charger to power your laptop and cannot power peripheral devices.)
- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
- Quick Data Transfers: Accelerate your productivity with rapid data transfers using a high-speed 5Gbps USB 3.0 port and two 480Mbps USB 2.0 ports.
- 4K HDMI Display: Enhance your visual experience with a hub capable of delivering 4K resolution at 30Hz in both mirror and extend modes. Please note that this hub is compatible with MacBook (macOS 12 and newer), Windows 10 and 11, ChromeOS, and laptops equipped with DP Alt Mode and Power Delivery. Note: This device is not compatible with Linux.
- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
from sklearn.feature_selection import SelectKBest, f_regression
X_top = SelectKBest(f_regression, k=10).fit_transform(X, y)5. Rank non-negative features with chi-squared scoring
chi2 is a univariate score for classification, and its input features must be non-negative. If your data includes negative values, do not apply this snippet without an appropriate transformation.
from sklearn.feature_selection import SelectKBest, chi2
X_top = SelectKBest(chi2, k=10).fit_transform(X, y)6. Rank features by mutual information for classification
Mutual information can detect statistical dependence beyond the relationships captured by an F-test. It estimates dependence nonparametrically, so results require enough data for a useful estimate and correct treatment of discrete versus continuous features.
Rank #3
- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
from sklearn.feature_selection import SelectKBest, mutual_info_classif
X_top = SelectKBest(mutual_info_classif, k=10).fit_transform(X, y)7. Select features using model importance
SelectFromModel relies on an estimator that exposes feature importances or coefficients after fitting. This random-forest example uses the selector’s default threshold, which is estimator-dependent; for reproducible choices, set a threshold deliberately and validate it.
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
X_model = SelectFromModel(estimator=RandomForestClassifier()).fit_transform(X, y)8. Use L1-regularized logistic regression as a selector
L1 regularization can drive some logistic-regression coefficients to zero, giving SelectFromModel a sparse set of weights to use. This is a classification pattern, and coefficient-based selection can be affected by feature scaling.
Rank #4
- Dual Converters, Infinite Potential：Includes 2× USB C male to USB A female adapters and 2× USB A male to USB C female adapters. Perfect for a wide range of uses—tablets with Bluetooth keyboards, expand USB ports on macbook, and more. Two different converters for all your daily needs
- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
- Upgraded Exquisite Craftsmanship: With an aluminum alloy housing and metal connector, the usbc to usb adapter is extremely durable and sturdy. Rigorously tested to withstand more than 10,000 times of plugging and unplugging, ensuring long-lasting performance
- Broad Compatible: The usb c to usb adapter widely supports all USB C/ USB A devices like laptops, tablets, cellphones, car chargers, and phone chargers. Such as compatible with MacBook Pro/Air 2023/2022, Thunderbolt 4/3 Devices,Apple MagSafe Watch 9/8/7/SE/Ultra, iPad Pro 2022/2021, Samsung Galaxy S23/S20/S10, and iPhone 17/16/15 Pro. Plug and play
- Please Note: To reach 10Gbps speed, keep the cable under 3.3 ft. For USB A Male to USB C adapters, try flipping the USB C connector. USB C Male to USB A adapters support bidirectional 10Gbps transfer within 3.3 ft
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
X_l1 = SelectFromModel(
LogisticRegression(penalty="l1", solver="liblinear")
).fit_transform(X, y)9. Recursively eliminate features to a chosen count
Recursive Feature Elimination (RFE) repeatedly fits an estimator and removes features according to its feature weights. The estimator must expose coefficients or feature importances.
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
X_rfe = RFE(
estimator=LogisticRegression(), n_features_to_select=10
).fit_transform(X, y)10. Put selection and prediction in one pipeline
A pipeline lets cross-validation fit the selector using each training fold and then apply it to that fold’s held-out data. This is the safe pattern for evaluating selection with cross-validation.
Best Value
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
pipe = make_pipeline(SelectKBest(f_classif, k=10), LogisticRegression())
scores = cross_val_score(pipe, X, y, cv=5)Which selection family should you use?
| Family | What it uses | Useful distinction | Watch out for |
|---|---|---|---|
| Variance filter | Features in X only |
Removes constant or low-variance columns without using the target | Threshold is scale-sensitive and says nothing about target relevance |
| Univariate filter | A per-feature score against y |
Simple feature ranking; SelectKBest keeps a chosen count |
Each feature is scored individually; match the score to target type and assumptions |
| Mutual information | Estimated feature-target dependence | Can represent broader statistical dependence than an F-test | Nonparametric estimates need enough data and suitable discrete-feature declarations |
| Model-based | Estimator coefficients or importances | Selection reflects a chosen model’s weights | Results depend on estimator, threshold and, for coefficient models, feature scales |
| Recursive or sequential | Repeated model fits or feature-subset evaluation | Can account for model performance as features are selected | Usually costs more fitting work; selection must remain inside validation folds |
These examples are one-line patterns, not 10 distinct algorithms: several use the same selector with a different score or configuration. Other scikit-learn options include SelectPercentile, SelectFdr, RFECV and SequentialFeatureSelector. Sequential approaches can require substantially more model fitting than a simple filter, so consider computation as well as assumptions and validation results.
Prevent feature-selection leakage
The scikit-learn Common pitfalls and recommended practices guide says: “As with any other type of preprocessing, feature selection should only use the training data.” If a selector is fit on the full dataset before a split, information from the eventual validation or test set can influence which features are chosen, making an evaluation misleading.
Scikit-learn’s current Common pitfalls documentation, shown as version 1.9.1, illustrates the problem with 200 samples and 10,000 random features: selecting before splitting reports 0.76 accuracy, while splitting first and fitting selection on training data reports 0.5. These are outputs from a synthetic leakage demonstration with random targets, not general benchmarks or expected performance figures.
- Choose the selector and prediction estimator for the task.
- Place both in a scikit-learn
Pipelineormake_pipeline. - Pass the complete pipeline—not a preselected matrix—to
cross_val_scoreor a model-selection routine. - For a final holdout evaluation, split first, fit the pipeline on training data, and score it on the untouched test data.
Choose and evaluate a selector
- Use a variance filter when the aim is to remove constant or near-constant columns, not to identify features predictive of
y. - For supervised filters, match the score to classification or regression; use
chi2only with non-negative features. - Use mutual information when broader dependence is useful and the sample size and feature-type settings support a meaningful estimate.
- Use model-based or recursive selection when the chosen estimator’s weights or repeated evaluation are relevant to the modeling objective; account for scaling and extra fit cost.
- Compare selectors through the same leakage-safe validation procedure. A selected feature count or a high training score alone does not establish that a selector will generalize.
For release-specific behavior, check the installed version’s VarianceThreshold, SelectKBest, feature-selection API and SelectFromModel documentation. The SelectFromModel link is the development documentation, so verify defaults against your installed release before relying on them.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
