10 Python One-Liners for Machine Learning Modeling  - MachineLearningMastery.com (2025)

Building machine learning models is an undertaking which is now within everyone’s reach. All it takes is some knowledge of the fundamentals of this area of artificial intelligence (AI) along with some programming skills. For constructing machine learning models programmatically, elegantly, and compactly, Python is usually a first choice today.

This article takes an insightful, practical tour through common Python programming practices contextualized to building machine learning models. Concretely, we examine Python’s capabilities for writing one-liners — single lines of code that accomplish meaningful tasks efficiently and concisely — to expound 10 common and helpful one-liners to keep in mind to build, evaluate, and validate models capable of learning from data.

1. Load a Pandas DataFrame from a CSV Dataset

Most classical machine learning models make use of structured or tabular data. In these cases, the Pandas library is definitely a handy solution for storing these data into DataFrame objects, ideally suited to contain structure row-column data observations. This one-liner is therefore one of your likely initial lines of code when writing a program to build a machine learning model.

Here, the path to the dataset can be an URL to a public dataset (for instance, one available as a raw file in a GitHub repository) or a local file to the programming environment.

Sometimes, libraries for machine learning modeling like Scikit-learn provide a catalog of sample datasets, such as the iris dataset for classifying flower species. In these cases, the above one-liner can be used like this with additional arguments to specify what the data attributes’ names are:

2. Remove Missing Values

A common issue found in real-world datasets is the existence of entries with missing values for one or several of its attributes. While there are strategies for estimating (imputing) these values, in some contexts, it may be a better solution to simply remove data instances containing missing values, especially if we are in a non-high-stakes scenario where the proportion of observations containing missing values is very small.

At first, some may think you’ll need a loop to go through the entire dataset and check, row by row, whether there are missing values or not. Far from that, this simple one-liner can be applied to a dataset contained in a Pandas DataFrame to automatically remove all such entries in one go.

Here we are creating a new DataFrame (df_clean) from the original DataFrame (df), minus rows with missing values (dropna()). Read more about the dropna() function here.

3. Encode Categorical Features Numerically

One-hot encoding is a common approach to encoding categorical features like size (small, medium, and large, for instance) into multiple binary attributes that indicate via values of 1 (resp. 0) whether the instance belongs or not to each of the possible categories in the original feature.

For example, a pizza instance of medium size can be described — instead of using the categorical feature size — using three one-hot encoded features, one for each possible size (small_size, medium_size, large_size), such that this pizza has a value of 1 for the new feature size_medium, and 0 for the other two new features associated to small and large sizes. Pandas offers the get_dummies() function to this seamlessly.

In the above code, the get_dummies() function accepts the original DataFrame (df), drops the header row (drop_first=True), and returns a one-hot-encoded DataFrame that gets assigned to df_encoded.

4. Split a Dataset for Training and Testing

This is extremely important when building any machine learning model: we must split our original dataset such that only part of it is used for training the model, and then the rest is used to make some test predictions and have a glimpse of its performance when exposed to future unseen data. With the aid of the Scikit-learn library, and its model_selection module, this partitioning process couldn’t be made any easier using the train_test_split() function.

The above example randomly splits the data observations into a training set containing 80% of the original observations and a test set housing the remaining 20% instances. Read more about the various parameters and options for train_test_split() here.

5. Initialize and Train a Scikit-learn Model

You don’t need to first initialize your machine learning — say, for example, a logistic regression classifier — and then train it in a separate instruction. You can do both at once like this.

Think of the time and lines of code you’ll save!

6. Evaluate Model Accuracy on Test Data

Once you have used your training data and labels to build a machine learning model, this one-liner can be used to have a quick view of its accuracy on the test data that we kept aside earlier upon splitting the original dataset.

While this can be valid for a sneak peek at the model’s performance, in most real-world applications, you may want to use a combination of several, more sophisticated metrics to have a comprehensive understanding on how your model performs against different types of data.

7. Apply Cross-validation

Cross-validation is a more systematic and rigorous approach to carefully assessing the performance of your machine learning and, more importantly, its ability to generalize well to new data it is exposed to in the future.

This one-liner provides a very quick approach to performing cross-validation by simply specifying the model to validate, the test data and labels, as well as the number of folds your data should be split into during the validation process.

For more information about cross-validation, check here.

8. Make Predictions

This is a pretty easy one, but it is indispensable to make use of your newly built machine learning model! The Scikit-learn predict() function accepts a set of test data instances and returns a list of predictions for them.

You may typically use the returned list of predictions (preds) to compare them against the actual labels of those observations, hence obtaining an objective measurement of the model’s accuracy.

9. Feature Scaling

Many machine learning models work better when data are first standardized into a common scale, particularly when the numerical ranges vary greatly from one feature to another. This is how you can do this in a single line using Scikit-learn’s StandardScaler objects.

The resulting X_scaled DataFrame will have scaled the X DataFrame features by removing the mean and scaling to unit variance, as calcaluted by:
\[
z = \frac{x – \mu}{\sigma}
\]

Read more about Scikit-learn’s StandardScaler here.

10. Building Preprocessing and Model Training Pipelines

This one looks pretty cool (in this writer’s opinion), but its applicability and interpretability depend on the complexity of the process you need to encapsulate into a single pipeline. Scikit-learn’s make_pipeline() function creates Pipeline objects from estimators.

The above pipeline manages the dataset’s feature scaling, model initialization, and model training as a unified process.

This is particularly recommended for pipelines in which relatively straightforward data preparation and model training stages can be easily chained together. Contrast the relatively easy to understand pipeline above with the following:

In this “unreasonable” pipeline:

• SimpleImputer(strategy="constant", fill_value=-1): replaces missing data with an arbitrary sentinel
• PolynomialFeatures(degree=6): creates 6th-degree interaction terms, exploding the feature space
• StandardScaler(with_std=False): centers each feature (subtracts the mean) but skips scaling by the standard deviation
• PCA(n_components=8): reduces the huge polynomial space back down to 8 principal components
• MinMaxScaler(feature_range=(0, 10)): rescales those components into the range [0, 10]
• SelectKBest(score_func=f_classif, k=4): picks the top 4 features via the ANOVA F-test
• LogisticRegression(elasticnet): trains with a mix of L1/L2 penalty, using an unusually high max_iter for convergence
• CalibratedClassifierCV(method="isotonic", cv=4): wraps the logistic model to recalibrate its probability outputs using 4-fold isotonic regression

This pipeline is excessively complex and opaque, making it difficult to comprehend how the individual layered meta-estimators affect the final result — not to mention that many of these additional estimators are redundant and have made the resulting model prone to overfitting.

Conclusion

This article took a look at ten effective Python one-liners that, once familiar with, will boost and simplify your process of building machine learning models, from data collection and preparation to the process of training your model, to evaluating and validating it based on test predictions.