Scikit-learn, often referred to as scikit, is a Python library that provides simple and efficient tools for data analysis and modeling. It’s widely used in data science for machine learning algorithms, data preprocessing, and performance metrics.
Data Science Made Simple: 5 essential Scikit-learn tricks
6 mins read
Oct 29, 2025
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This article was written for Pathrise, an online mentorship program that works with students and professionals on every component of their job search.
Scikit-learn (also called sklearn) is the most popular Python machine learning library for data science. Any data scientist or machine learning engineer needs Scikit in their tool belt. For many big companies, like J.P. Morgan, Spotify, Hugging Face, and more, Scikit-learn is an indispensable part of their product development.
Understanding this tool can open doors for employment in the data science world and help you land a data science job more easily.
Sklearn provides flexible tools for learning, improving, and executing our machine learning models. This article will take your Sklearn skills to the next level with some insider tips and tricks. These best practices will excel your machine learning skills and make your programming life easier.
Today we will cover the following 5 best practices and tricks:
- Imputing missing values with iterative imputer
- Generating random dummy data
- Using Pickle for model persistence
- Plotting a confusion matrix
- Creating visualizations for decision trees
- What to learn next
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1. Imputing missing values with iterative imputer#
When a dataset has missing values, many problems in an ML algorithm can occur. In each column, we need to identify and replace missing values before we model prediction tasks. This process is called data imputation.
It’s easy to stick with traditional methods for imputing missing values, like mode (for classification) or the mean/median (for regression). But Sklearn provides more powerful, simpler ways to impute missing values.
In Sklearn, the IterativeImputer class allows us to use an entire set of features to locate and eliminate missing values. In fact, it is specifically designed to estimate missing values by taking them as a function of other features.
This approach repeatedly defines a model to predict missing features as a function of other features. This improves our dataset with each iteration.
To use this built-in iterative imputation feature, you must import enable_iterative_imputer, since it is still in the experimental phase.
>>> # explicitly require this experimental feature>>> from sklearn.experimental import enable_iterative_imputer>>> # now you can import normally from sklearn.impute>>> from sklearn.impute import IterativeImputer
Take a look at this code example below to see how simple IterativeImputer is. With this code, any missing values in a dataframe will be filled in a new dataframe called impute_df.
We set the number of iterations and verbosity, which is optional. The imputer is fitted to the dataset that has missing values, generating our new dataframe.
from sklearn.experimental import enable_iterative_imputerfrom sklearn.impute import IterativeImputerdf = pd.DataFrame(*a dataset with missing values that we want to impute*)imp = IterativeImputer(max_iter=10, verbose=0)imp.fit(df)impute_df = imp.transform(df)impute_df = pd.DataFrame(impute_df, columns=df.columns)
2. Generating random dummy data#
Dummy data refers to datasets that do not contain useful data. Instead, they reserve space where real or useful data should be present. Dummy data is a placeholder for testing, so it must be evaluated carefully to prevent unintended results.
Sklearn makes it easy to generate reliable dummy data. We simply use the functions make_classification() for classification data or make_regression() for regression data. You’ll also want to set the parameters, like the number of samples and features.
These functions give us control over the behavior of your data, so we can easily debug or test on small datasets.
Look at the code example below with 1,000 samples and 20 features.
from sklearn.datasets import make_classificationX, y = make_classification(n_samples=1000, n_features=20)
3. Using Pickle for model persistence#
Model resistance allows us to reuse an ML model without retraining it. Sklearn’s Pickle model allows us to reuse a model and achieve model persistence. Once you save a model as a Pickle, it can be easily loaded later at any time for more predictions.
To serialize your algorithms and save them, you can use either the pickle or joblib Python libraries. Take a look at the code example below.
import pickle# Save a KNN modelsaved_model = pickle.dumps(knn)# Load a saved KNN modelload_model = pickle.loads(saved_model)# Make new predictions from a pickled modelload_model.predict(test_X)
4. Plotting a confusion matrix#
A confusion matrix is a table that describes a classifier’s performance for test data. Sklearn’s most recent release adds the function plot_confusion_matrix to generate an accessible and customizable confusion matrix that maps our true positive, false positive, false negative, and true negative values.
Take a look at this code example from the Sklearn documentation.
>>> from sklearn.metrics import confusion_matrix>>> y_true = [2, 0, 2, 2, 0, 1]>>> y_pred = [0, 0, 2, 2, 0, 2]>>> confusion_matrix(y_true, y_pred)array([[2, 0, 0],[0, 0, 1],[1, 0, 2]])
5. Creating visualizations for decision trees#
The decision tree is one of the most popular classification algorithms for data science. In this algorithm, the training model learns to predict values of the target variable by learning decision rules with a tree representation. A tree is made up of nodes with corresponding attributes.
We can now visualize decision trees with matplotlib using tree.plot_tree. This means you don’t have to install any dependencies to create simple visualizations. You can then save your tree as a .png file for easy access.
Take a look at this example from the Sklearn documentation. The example visual decision tree should give you the basic structure of what Scikit-learn generates (see the official documentation for further details).
tree.plot_tree(clf)
Building robust preprocessing pipelines#
As projects scale, manually handling preprocessing in separate steps can become messy and error-prone. Scikit-learn’s Pipeline and ColumnTransformer make it easy to combine data cleaning, feature engineering, and modeling into a single reproducible workflow.
Key techniques to include:
Use Pipeline to chain transformations and model training.
Handle numeric and categorical data simultaneously with ColumnTransformer.
Ensure transformations are applied consistently during training and prediction.
This is now considered a best practice for any production-level project and keeps your workflow maintainable.
Smarter hyperparameter tuning techniques#
Model performance often depends more on hyperparameter tuning than on the choice of algorithm itself. Instead of relying solely on GridSearchCV, newer approaches offer better results with less computation.
Include these techniques:
RandomizedSearchCV for faster hyperparameter search.
Successive halving and HalvingGridSearchCV for adaptive tuning.
Integrating with Optuna or scikit-optimize for more efficient optimization.
These approaches can significantly boost performance while saving training time.
Handling categorical data and missing values#
Real-world datasets often include categorical features, null values, or unseen categories at inference time. Scikit-learn now provides robust tools for these cases:
OneHotEncoder with handle_unknown='ignore' to prevent runtime errors.
Built-in support for missing values in estimators like HistGradientBoostingClassifier.
KNNImputer as an alternative to IterativeImputer for certain data types.
These features make your models more resilient to messy real-world data.
Advanced model evaluation and interpretability#
Evaluating models goes far beyond confusion matrices. Modern ML projects demand deeper insights into performance and decision-making.
New evaluation techniques to cover:
Precision-recall curves, calibration plots, and multiclass ROC curves.
Feature importance visualization and permutation importance.
Using SHAP or LIME for model interpretability.
These tools help you explain why your model behaves the way it does, which is critical for debugging, trust, and deployment.
Combining scikit-learn with modern ML ecosystems#
Scikit-learn now plays a critical role in larger workflows that include deep learning, feature stores, and model serving. Show how it integrates with:
Hugging Face Transformers for feature extraction.
XGBoost or LightGBM as complementary model choices.
Pandera or Great Expectations for data validation in preprocessing pipelines.
This gives readers a more holistic view of how scikit-learn fits into real-world ML systems.
What to learn next#
Congrats! You’ve now learned a lot more about Sklearn and are ready to take your machine learning skills to the next level. There is still a lot to learn about Scikit to get the most out of this powerful library.
A good next step is to explore more Scikit tricks, learn Seaborn and Keras, and take an online course to solidify your learning.
Educative’s course Hands-on Machine Learning with Scikit-learn will help you dive deeper into linear regression, logistic regression, k-means clustering, and more. By the end, you’ll be able to confidently use Sklearn in your own projects.
Or, if you are ready for more advanced content, check out Educative’s course Grokking the Machine Learning Interview to learn how to apply ML concepts to real-world system design situations that you can expect in an ML interview.
Happy learning!
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Frequently Asked Questions
What is the role of scikit in data science?
What is the role of scikit in data science?
Written By:
Amanda Fawcett
