A Comprehensive Guide to Machine Learning Model Visualization

Originally from Data Analysis and Applications

Machine learning models have powerful and complex mathematical structures. Understanding their intricate workings is an important aspect of model development. Model visualization is crucial for gaining insights, making informed decisions, and effectively communicating results.

In this article, we will delve into the art of machine learning visualization, exploring various techniques that help us understand complex data-driven systems. At the end of the article, we also provide practical code for a visualization example.

What is Visualization in Machine Learning?

Machine learning visualization (ML visualization for short) generally refers to the process of representing machine learning models, data, and their relationships through graphical or interactive means. The goal is to make it easier to understand the complex algorithms and data patterns of the model, making it more accessible to both technical and non-technical stakeholders.

Visualization bridges the gap between the mysterious inner workings of machine learning models and our visual understanding of patterns.

The main functions of visualizing ML models include:

• Model Structure Visualization: Common model types such as decision trees, support vector machines, or deep neural networks typically consist of many computational and interactive layers that are difficult for humans to grasp. Visualization allows us to more easily see how data flows through the model and where transformations occur.

• Visualizing Performance Metrics: Once we have trained a model, we need to evaluate its performance. Visualizing metrics such as accuracy, precision, recall, and F1 score helps us understand how the model is performing and where improvements are needed.

• Comparative Model Analysis: When dealing with multiple models or algorithms, visualizing structural or performance differences allows us to select the best model or algorithm for a specific task.

• Feature Importance: Understanding which features have the greatest impact on the model’s predictions is crucial. Visualization techniques like feature importance plots can easily identify the key factors driving model results.

• Interpretability: Due to their complexity, ML models are often a “black box” for human creators, making it hard to explain their decisions. Visualization can clarify how specific features influence outputs or the robustness of model predictions.

• Facilitating Communication: Visualization is a universal language for conveying complex ideas simply and intuitively. They are essential for effectively sharing information with management and other non-technical stakeholders.

Model Structure Visualization

Understanding how data flows through a model is crucial for grasping how machine learning models convert input features into their outputs.

Decision Tree Visualization

Decision trees have a flowchart-like structure that most people are familiar with. Each internal node represents a decision based on specific feature values. Each branch in the node represents the outcome of that decision. Leaf nodes represent the model’s output.

Visualizing this structure provides a direct representation of the decision-making process, enabling data scientists and business stakeholders to understand the decision rules learned by the model.

During training, decision trees identify the features that best separate samples in branches based on specific criteria (usually Gini impurity or information gain). In other words, it determines the most discriminative features.

Visualizing decision trees (or their collections, such as random forests or gradient-boosted trees) involves graphical rendering of their overall structure, clearly displaying the splits and decisions at each node. The depth and width of the tree and the leaf nodes are immediately apparent. Additionally, decision tree visualization helps identify key features that are the most discriminative attributes leading to accurate predictions.

The path to accurate predictions can be summarized in four steps:

• Clear Features: Decision tree visualization is like peeling back layers of complexity to reveal key features. It’s akin to viewing a decision flowchart, where each branch represents a feature, and each decision node contains critical aspects of our data.

• Discriminative Attributes: The beauty of decision tree visualization lies in its ability to highlight the most discriminative features. These factors significantly influence the outcome, guiding the model’s predictions. By visualizing the tree, we can pinpoint these features, thus understanding the core factors driving model decisions.

• Path to Accuracy: Each path on the decision tree is a journey toward accuracy. Visualization showcases the sequence of decisions leading to a specific prediction. This is the gold standard for understanding the logic and criteria our model uses to arrive at specific conclusions.

• Simplicity in Complexity: Although machine learning algorithms are complex, decision tree visualization possesses simplicity. It transforms intricate mathematical computations into intuitive representations, making them accessible to both technical and non-technical stakeholders.

Example of decision tree visualization in machine learning: Decision tree classifier trained on the Iris dataset | Source: Author

The above image shows the structure of a decision tree classifier trained on the famous Iris dataset. This dataset consists of 150 samples of iris flowers, each belonging to one of the following three species: setosa, versicolor, or virginica. Each sample has four features: sepal length, sepal width, petal length, and petal width.

From the decision tree visualization, we can understand how the model classifies the flowers:

1. Root Node: At the root node, the model determines whether the petal length is less than or equal to 2.45 cm. If so, it classifies the flower as setosa. Otherwise, it moves to the next internal node.

2. Second Split Based on Petal Length: If the petal length is greater than 2.45 cm, the tree again uses this feature to make a decision. The criterion is whether the petal length is less than or equal to 4.75 cm.

1. Split Based on Petal Width: If the petal length is less than or equal to 4.75 cm, the model then considers the petal width and determines whether it is greater than 1.65 cm. If so, it classifies the flower as virginica. Otherwise, the model’s output is versicolor.

1. Split Based on Sepal Length: If the petal length is greater than 4.75 cm, the model during training determined that sepal length is most suitable for distinguishing between flower species and virginica. If the sepal length is greater than 6.05 cm, it classifies the flower as virginica. Otherwise, the model’s output is versicolor.

Visualization captures this hierarchical decision-making process and presents it in a way that is easier to understand than a simple list of decision rules.

Ensemble Model Visualization

Ensemble methods like random forests, AdaBoost, gradient boosting, and bagging combine multiple simpler models (called base models) into a larger, more accurate model. For example, a random forest classifier consists of many decision trees. Understanding the contributions and complex interactions of the models that make up the ensemble is crucial for debugging and evaluating it.

One way to visualize ensemble models is to create a chart showing how base models contribute to the output of the ensemble model. A common approach is to plot the decision boundaries of the base models (also known as surfaces), highlighting their influence on different parts of the feature space. By studying how these decision boundaries overlap, we can understand how the base models contribute to the collective predictive ability of the ensemble.

Ensemble model visualization can also help users better understand the weights assigned to each base model in the ensemble. Typically, base models have a strong influence in certain areas of the feature space while having little impact in others. However, there may also be base models that have never made a significant contribution to the ensemble output. Identifying base models with particularly low or high weights helps make the ensemble model more robust and improve its generalization.

Intuitive Model Building

Visual ML is a method of designing machine learning models using low-code or no-code platforms. It enables users to create and modify complex machine learning processes, models, and outcomes through a user-friendly visual interface. Visual ML does not retroactively generate model structure visualizations; instead, it places them at the core of the ML workflow.

In short, Visual ML platforms provide a drag-and-drop model-building workflow that allows users from various backgrounds to easily create ML models. They bridge the gap between the abstract world of algorithms and our innate ability to grasp patterns and relationships visually.

These platforms can save us time and help us quickly build model prototypes. Since models can be created in minutes, it is easy to train and compare different model configurations. The best-performing model can then be further optimized, perhaps using a more code-centric approach.

Data scientists and machine learning engineers can utilize Visual ML tools to create:

• 1 Experimental prototypes

• 2 MLOps pipelines

• 3 Generate optimal ML code for production

• 4 Extend existing ML model codebases for larger examples

Examples of Visual ML tools include TensorFlow’s Neural Network Playground and KNIME, which is an open-source data science platform built entirely around Visual ML and no-code concepts.

Visualizing Machine Learning Model Performance

In many cases, we are less concerned with how the model works internally and more interested in understanding its performance. Which samples are reliable? Where does it frequently draw incorrect conclusions? Should we choose model A or model B?

In this section, we will introduce machine learning visualization effects that help us better understand model performance.

Confusion Matrix

The confusion matrix is a fundamental tool for evaluating the performance of classification models. It compares the model’s predictions with the ground truth, clearly showing which samples the model misclassified or struggled to distinguish between categories.

For binary classifiers, the confusion matrix has only four fields: true positive, false positive, false negative, and true negative:

With this information, we can directly calculate basic metrics such as accuracy, recall, F1 score, and precision.

The confusion matrix for multi-class models follows the same general idea. The diagonal elements represent correctly classified instances (i.e., model outputs matching the true values), while the non-diagonal elements represent misclassifications.

Below is a small snippet to generate a confusion matrix for a scikit-learn classifier:

import matplotlib.pyplot as pltfrom sklearn.datasets import make_classificationfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplayfrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVC

# generate some sample dataX, y = make_classification(n_samples=1000,n_features=10,n_informative=6,n_redundant = 2,n_repeated = 2,n_classes = 6,n_clusters_per_class=1,random_state = 42)

# split the data into train and test setX_train, X_test, y_train, y_test = train_test_split(X, y,random_state=0)

# initialize and train a classifierclf = SVC(random_state=0)clf.fit(X_train, y_train)

# get the model’s prediction for the test setpredictions = clf.predict(X_test)

# using the model’s prediction and the true value,# create a confusion matrixcm = confusion_matrix(y_test, predictions, labels=clf.classes_)

# use the built-in visualization function to generate a plotdisp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)disp.plot()plt.show()

Let’s take a look at the output. As mentioned earlier, the elements on the diagonal represent the true classes, while the elements outside the diagonal indicate where the model confused classes, hence the name “confusion matrix.”

Here are three key points about the plot:

1. Diagonal: Ideally, the main diagonal of the matrix should be filled with the highest numbers. These numbers indicate the instances where the model correctly predicted the class, aligning with the true classes. It appears our model is doing well here!

2. Non-Diagonal Entries: The numbers outside the main diagonal are equally important. They reveal where the model is making mistakes. For example, if we look at the cell where the 5th row intersects with the 3rd column, we see 5 instances where the true class is “5,” but the model predicted the class as “3.” Perhaps we should examine the affected samples to better understand what is happening here!

3. Instant Performance Analysis: By checking non-diagonal entries, you can immediately see they are quite low. Overall, the classifier seems to be performing well. You will also notice that the sample sizes for each of our categories are roughly equal. In many real-world scenarios, this is not the case. Then, generating a second confusion matrix showing the likelihood of correct classification (rather than the absolute number of samples) may be helpful.

Visual enhancements like color gradients and percentage annotations make the confusion matrix more intuitive and easier to interpret. A confusion matrix styled like a heatmap draws attention to classes with high error rates, guiding further model development.

The confusion matrix can also help non-technical stakeholders grasp the model’s strengths and weaknesses, facilitating discussions about whether additional data or precautions are needed when using model predictions for critical decisions.

Visualizing Clustering Analysis

Clustering analysis groups similar data points based on specific features. Visualizing these clusters can reveal patterns, trends, and relationships in the data.

Coloring each point in a scatter plot according to its cluster assignment is a standard method of visualizing clustering analysis results. The clustering boundaries and their distribution in the feature space are clearly visible. Pair plots or parallel coordinates help understand relationships between multiple features.

A popular clustering algorithm, k-means, starts with selecting k samples from the dataset as initial centroids.

Once these initial centroids are established, k-means alternates between two steps:

• 1 It associates each sample with the nearest centroid, thereby creating clusters composed of samples associated with the same centroid.

• 2 It recalibrates the centroids by averaging the values of all samples in the cluster.

As this process continues, the centroids move, and points’ associations with clusters are iteratively refined. Once the difference between the new and old centroids falls below a set threshold, stability is achieved, and k-means ends.

The result is a set of centroids and clusters that you can visualize as shown in the above figure.

For larger datasets, techniques like t-SNE (t-distributed Stochastic Neighbor Embedding) or UMAP (Uniform Manifold Approximation and Projection) can be used to reduce dimensions while preserving clustering structures. These techniques help effectively visualize high-dimensional data.

t-SNE takes complex high-dimensional data and transforms it into a low-dimensional representation. The algorithm first assigns a position to each data point in the low-dimensional space. It then looks at the original data and considers its neighboring points, deciding each point’s actual position in this new space. Points that are similar in high-dimensional space are pulled closer together in the new space, while those that differ are pushed apart.

This process is repeated until points find their perfect positions. The final result is a clustering representation where similar data points form groups, allowing us to see the patterns and relationships hidden in high-dimensional chaos. It’s like a symphony where each note finds its harmonious place, creating a beautiful composition of data.

UMAP also seeks to find clusters in high-dimensional space but takes a different approach. Here’s how UMAP works:

• Neighbor Search: UMAP first identifies the neighbors of each data point. It determines which points are close to each other in the original high-dimensional space.

• Fuzzy Simple Set Construction: Imagine creating a network of connections between these neighboring points. UMAP models the strength of these connections based on the relevance or similarity of points.

• Low-Dimensional Layout: After determining their proximity, UMAP carefully arranges data points in low-dimensional space. In this new space, points that are closely connected in high-dimensional space are placed closely together.

• Optimization: UMAP aims to find the best representation in lower dimensions. It minimizes the distance discrepancies between the original high-dimensional space and the new low-dimensional space.

• Clustering: UMAP uses clustering algorithms to group similar data points. Imagine gathering marbles of similar colors together—this allows us to see patterns and structures more clearly.

Comparative Model Analysis

Comparing different model performance metrics is crucial for determining which machine learning model is best suited for the task. Whether in the experimental phase of a machine learning project or retraining production models, visualization is often needed to transform complex numerical results into actionable insights.

Therefore, visualizations of model performance metrics, such as ROC curves and calibration plots, are essential tools that every data scientist and machine learning engineer should have in their toolbox. They are fundamental to understanding and communicating the effectiveness of machine learning models.

ROC Curve

When analyzing machine learning classifiers and comparing ML model performance, the Receiver Operating Characteristic (ROC) curve is crucial.

The ROC curve compares the model’s true positive rate against its false positive rate as a function of the cutoff threshold. It describes the trade-offs we always have to make between true positives and false positives, providing insights into the model’s discriminative ability.

A curve close to the upper left corner indicates excellent performance: the model achieves a high true positive rate while maintaining a low false positive rate. Comparing ROC curves helps us select the best model.

Here’s a step-by-step explanation of how the ROC curve works:

In binary classification, we are interested in predicting one of two possible outcomes, typically labeled as positive (e.g., presence of disease) and negative (e.g., absence of disease).

Keep in mind that we can convert any classification problem into a binary one by selecting one class as the positive outcome and designating all other classes as negative. Therefore, the ROC curve is still helpful for multi-class or multi-label classification problems.

The axes of the ROC curve represent two metrics:

• True Positive Rate (Sensitivity): The proportion of actual positive cases that the model correctly identifies.

• False Positive Rate: The proportion of actual negative cases that are incorrectly identified as positive.

Machine learning classifiers typically output the likelihood that a sample belongs to the positive class. For instance, values output by a logistic regression model range between 0 and 1, which can be interpreted as probabilities.

As data scientists, we are responsible for selecting a threshold above which we assign a positive label. The ROC curve shows us how this choice impacts classifier performance.

If we set the threshold to 0, all samples will be assigned to the positive class, resulting in a false positive rate of 1. Thus, in any ROC curve plot, you will see the curve ending at (1, 1).

If we set the threshold to 1, no samples will be assigned to the positive class. However, since we will never incorrectly assign negative samples to positive in this case, the false positive rate will be 0. You may have guessed that this is where we see the curve starting at (0, 0) in the ROC plot.

By varying the threshold for classifying samples as positive, we plot the curve between these points. The resulting curve (ROC curve) reflects how the true positive rate and false positive rate change with variations in the threshold.

But what have we learned from this?

The ROC curve shows us the trade-offs we must make between sensitivity (true positive rate) and specificity (1 – false positive rate). In simpler terms, we can find all positive samples (high sensitivity) or ensure that all samples identified as positive actually belong to the positive class (high specificity).

Consider a classifier that perfectly distinguishes between positive and negative samples: its true positive rate is always 1, and its false positive rate is always 0, regardless of the threshold we choose. Its ROC curve will rise from (0,0) straight up to (0,1), then form a straight line between (0,1) and (1,1).

Thus, the closer the ROC curve is to the left and top edges of the plot, the stronger the model’s discriminative ability and the better it meets the sensitivity and specificity goals.

To compare different models, we typically do not use the curves directly but instead calculate the area under the curve. This quantifies the model’s overall ability to distinguish between positive and negative classes.

This so-called ROC-AUC (Area Under the ROC Curve) can take values between 0 and 1, with higher values indicating better performance. In fact, our perfect classifier would achieve exactly 1 for the ROC-AUC.

When using the ROC-AUC metric, it is essential to remember that the baseline is not 0 but 0.5—the ROC-AUC of a completely random classifier. If we use np.random.rand() as a classifier, the generated ROC curve will be a diagonal line from (0,0) to (1,1).

Generating ROC curves and calculating ROC-AUC using scikit-learn is straightforward. Just a few lines of code in the model training script can create this evaluation data for each training run. When logging the ROC-AUC and ROC curve plots with ML experiment tracking tools, you can later compare different model versions.

Experiment Tracking

It is very useful to keep an orderly record of all experiments when visualizing, comparing, and debugging models.

Calculate and log ROC-AUCfrom sklearn.metrics import roc_auc_score

clf.fit(x_train, y_train)

y_test_pred = clf.predict_proba(x_test)auc = roc_auc_score(y_test, y_test_pred[:, 1])

# optional: log to an experiment-tracker like neptune.aineptune_logger.run["roc_auc_score"].append(auc)
Create and log ROC plotfrom scikitplot.metrics import plot_rocimport matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(16, 12))plot_roc(y_test, y_test_pred, ax=ax)

# optional: log to an experiment tracker like neptune.aifrom neptune.types import Fileneptune_logger.run["roc_curve"].upload(File.as_html(fig))

Calibration Curve

While machine learning classifiers typically output values between 0 and 1 for each class, these values do not represent statistical probabilities or confidence levels. In many cases, this is perfectly fine, as we are only interested in obtaining the correct labels.

However, if we want to report confidence levels along with classification results, we must ensure that our classifiers are calibrated. Calibration curves are useful visual aids for understanding how well a classifier is calibrated. We can also use them to compare different models or check whether our attempts to recalibrate a model were successful.

Let’s again consider the case of a model that outputs values between 0 and 1. If we choose a threshold, say 0.5, we can convert it into a binary classifier where all samples with higher outputs are assigned to the positive class (and vice versa).

The calibration curve plots the “positive scores” based on the model’s outputs. “Positive scores” are the conditional probabilities that a sample actually belongs to the positive class given the model output (P(sample belongs to positive class | model output between 0 and 1)).

Does this sound too abstract? Let’s look at an example:

First, look at the diagonal. It represents a perfectly calibrated classifier: the model’s outputs between 0 and 1 exactly match the probabilities of samples belonging to the positive class. For instance, if the model outputs 0.5, the sample has a 50:50 chance of being positive or negative. If the model outputs 0.2, the probability of the sample being positive is only 20%.

Next, consider the calibration curve of a Naive Bayes classifier: you will see that even if this model outputs 0, the sample has about a 10% chance of being positive. If the model outputs 0.8, there is still a 50% chance that the sample belongs to the negative class. Therefore, the classifier’s output does not reflect its confidence level.

Calculating “positive scores” is far from straightforward. We need to create bars based on the model’s outputs, which is complicated since the distribution of samples within the model’s output range is often not uniform. For example, a logistic regression classifier typically assigns values close to 0 or 1 to many samples but rarely outputs values close to 0.5. You can find a more in-depth discussion of this topic in the scikit-learn documentation. There, you can also explore possible methods for recalibrating models, which goes beyond the scope of this article.

For our purposes, we have learned how calibration curves visualize complex model behavior in an easily digestible way. By quickly glancing at the plot, we can see whether the model is well-calibrated and which model is closest to ideal.

Visualizing Hyperparameter Optimization

Hyperparameter optimization is a critical step in developing machine learning models. The goal is to select the best configuration of hyperparameters—generic names for parameters that are not learned from data but predefined by their human creators. Visualization can help data scientists understand how different hyperparameters affect model performance and properties.

Finding the best configuration of hyperparameters is a skill in itself, far beyond what we will focus on regarding machine learning visualization. To learn more about hyperparameter optimization, I recommend this article written by former Amazon AI researchers on improving ML model performance.

A common approach to systematic hyperparameter optimization is to create a list of possible parameter combinations and train a model for each parameter combination. This is often referred to as “grid search.”

For example, if you are training a Support Vector Machine (SVM), you may want to try different values for the parameters C (regularization parameter) and gamma (kernel coefficient):

import numpy as np C_range = np.logspace(-2, 10, 13)gamma_range = np.logspace(-9, 3, 13)
param_grid = {"gamma": gamma_range, "C": C_range}

Using scikit-learn’s GridSearchCV, you can train a model for each possible combination (using a cross-validation strategy) and find the best combination related to evaluation metrics:

from sklearn.model_selection import GridSearchCV,
grid = GridSearchCV(SVC(), param_grid=param_grid, scoring='accuracy')grid.fit(X, y)

After grid search is complete, you can check the results:

print(
"The best parameters are %s with a score of %0.2f"
% (grid.best_params_, grid.best_score_)
)

However, we are usually interested not just in finding the best model but also in understanding the impact of its parameters. For example, if one parameter does not affect model performance, we should not waste time and money trying more different values. On the other hand, if we see that as the parameter value increases, the model’s performance improves, we may want to try higher values for that parameter.

Below is a visualization of the grid search we just performed:

From the plot, it is evident that the value of gamma significantly impacts the performance of the support vector machine. If gamma is set too high, the influence radius of the support vectors is small, which can lead to overfitting even with a lot of regularization through C. In this case, the influence area of any support vector spans the entire training set, making the model resemble a linear model, using hyperplanes to separate dense regions of different classes.

The best model lies along the diagonal of C and gamma, as shown in the second plot panel. By adjusting gamma (lower values indicate smoother models) and increasing C (higher values emphasize correct classification), we can traverse this diagonal to obtain well-performing models.

Even from this simple example, you can see how useful visualization is for gaining insight into the fundamental reasons for differences in model performance. This is why many machine learning experiment tracking tools allow data scientists to create different types of visualizations to compare model versions.

Feature Importance Visualization

Feature importance visualization provides a clear and intuitive way to grasp the contribution of each feature in the model’s decision-making process. In many applications, it is crucial to understand which features significantly impact predictions.

There are many different ways to extract insights about feature importance from machine learning models. Broadly speaking, we can categorize them into two classes:

• Some types of models, such as decision trees and random forests, inherently contain feature importance information as part of their model structure. All we need to do is extract and visualize it.

• Most of the machine learning models currently in use do not provide out-of-the-box feature importance information. We must use statistical techniques and algorithmic methods to reveal the importance of each input feature to the model’s final output.

In the following sections, we will look at an example from each category: the impurity average reduction method for random forest models and the model-agnostic LIME interpretability method. Other methods you may want to explore include permutation importance, SHAP, and integrated gradients.

For the purposes of this article, we are less concerned with how to obtain feature importance data and more with its visualization. For this, bar charts are the preferred choice for structured data, with the length of each bar representing the importance of the feature. Heatmaps are clearly a favorite for images, while highlighting the most important words or phrases is typical for text data.

In a business context, feature importance visualization is a valuable tool for stakeholder communication. It provides a straightforward narrative showcasing the primary factors influencing predictions. This transparency enhances decision-making capabilities and can foster trust in model outcomes.

Feature Importance Assessment Using Impurity Average Reduction

The average reduction of impurity (impurity) is a metric for measuring each feature’s contribution to the performance of decision trees. To understand this, we first need to grasp what “impurity” means in this context.

Let’s start with an analogy:

• Imagine we have a fruit basket filled with apples, pears, and oranges. As the fruit pieces are placed in the basket, they are thoroughly mixed, and we can say that this set of fruits has high impurity.

• Now, our task is to classify them by type. If we put all the apples in one bowl, the oranges on a tray, and the pears in the basket, we are left with three perfectly pure sets of apples.

• But here’s the twist: we cannot see the fruits when making decisions. For each piece of fruit, we are told its color, diameter, and weight. Then we need to decide where it should go. Thus, these three attributes are our features.

• The weight and diameter of the fruit pieces will be very similar. They do not help us classify—put differently, they do not help reduce impurity.

• However, color will help. We may still struggle to distinguish between green or yellow apples and green or yellow pears, but if we learn that the color is red or orange, we can confidently make a decision. Thus, “color” will yield the greatest average reduction of impurity.

Now, let’s use this analogy in the context of decision trees and random forests:

When building decision trees, we want each node to be as pure as possible concerning the target variable. In simpler terms, when creating new nodes for our tree, our goal is to find the features that best separate the samples reaching the node into two different sets so that samples with the same label are in the same set. (For complete mathematical details, refer to the scikit-learn documentation).

Each node in the decision tree reduces impurity—roughly speaking, it helps rank the training samples by target labels. Suppose a feature serves as the decision criterion for many nodes in the tree and can effectively cleanly separate samples. In that case, it will account for a significant portion of the overall impurity reduction achieved by the decision tree. This is why looking at the “average impurity reduction” attributable to a feature is a good metric for measuring feature importance.

Wow, that’s quite complex!

Fortunately, the visualization is not hard to read.We can clearly identify the main drivers of the model and use this information in feature selection.Reducing the model’s input space to the most critical features can lower its complexity and help prevent overfitting.

Furthermore, understanding feature importance aids in data preparation. Features with lower importance may be candidates for removal or merging, thus simplifying the preprocessing of input data.

However, before we continue, I want to mention an important caveat. Since the impurity reduction at nodes is determined using training datasets, the “average impurity reduction” does not necessarily translate to unseen test data:

Suppose our training samples are numbered, and this numbering is the input feature of the model. If our decision tree is complex enough, it can know which sample has which label (e.g., “Fruit 1 is an orange”, “Fruit 2 is an apple”… The numerical features’ average impurity reduction will be enormous, and it will appear as a very important feature in our visualization, even though it is entirely useless when applying our model to data it has never seen before.

Local Interpretable Model-Agnostic Explanations (LIME)

Local interpretability methods aim to clarify how models behave for specific instances. (In contrast, global interpretability examines how models behave across their entire feature space).

One of the oldest and still widely used techniques is LIME (Local Interpretable Model-agnostic Explanations). To reveal the contribution of each input feature to the model’s predictions, a linear model is fitted that approximates the model’s behavior in a specific region of the feature space. Roughly speaking, the coefficients of the linear model reflect the importance of input features. The results can be visualized as feature importance plots, highlighting the features that most influence specific predictions.

Local interpretability techniques can extract intuitive insights from complex algorithms. The visualizations of these results can support discussions with business stakeholders or serve as a basis for cross-checking model learning behavior with domain experts. They provide practical, actionable insights that enhance trust in the complex inner workings of models and can become important tools for facilitating the adoption of machine learning.

How to Adopt Model Visualization in Machine Learning?

In this section, I will share tips on seamlessly integrating model visualization into daily data science and machine learning routines.

1. Start with Clear Objectives

Before diving into model visualization, define a clear purpose. Ask yourself, “What specific goals do I intend to achieve through visualization?

Are you seeking…

• …to improve model performance?

• …to enhance interpretability?

• …to better communicate results to stakeholders?

Defining objectives will provide direction for effective visualization.

2. Choose the Right Visualizations

Always adopt a top-down approach. This means starting from a very abstract level and then exploring deeper for more insights.

For example, if you are seeking to improve model performance, make sure to start with simple methods, such as plotting the model’s accuracy and loss using simple line charts.

Suppose yourmodel is overfitting. Then you can use feature importance techniques to rank features based on their contribution to model performance. You can plot these feature importance scores to visualize the most influential features in the model. Features with higher importance may point to overfitting and information leakage.

Similarly, you can create partial dependence plots for relevant features. PDP shows how predictions of the target variable change with variations in specific features while keeping other features constant. You should look for unstable behavior or drastic fluctuations in the curves, which may indicate overfitting due to that feature.

3. Select the Right Tools

Choosing the right tools depends on the task at hand and the capabilities offered by the tools. Python provides a wealth of libraries such as Matplotlib, Seaborn, and Plotly for creating static and interactive visualizations. Framework-specific tools (like TensorBoard for TensorFlow and scikit-plot for scikit-learn) are very valuable for model-specific visualizations.

4. Iterate and Improve

Remember, model visualization is an iterative process. Continuously optimize visualizations based on feedback from teams and stakeholders. The ultimate goal is to make your models transparent, interpretable, and accessible to all stakeholders. Their opinions and the ever-changing project requirements may mean you need to reconsider and adjust your approach.

Integrating model visualization into your daily data science or machine learning practice enables you to make clear, confident data-driven decisions. Whether you are a data scientist, domain expert, or decision-maker, adopting model visualization as a regular practice is a critical step in unlocking the potential of machine learning projects.

Conclusion

Effective machine learning model visualization is an indispensable tool for any data scientist. It enables practitioners to gain insights, make informed decisions, and transparently communicate results.

In this article, we introduced a wealth of information on how to visualize machine learning models. In summary, here are some key points:

The Purpose of Visualization in Machine Learning:

• Visualization simplifies complex ML model structures and data patterns for better understanding.

• Interactive visualizations and Visual ML tools enable users to dynamically interact with data and models. They can adjust parameters, zoom in on details, and better understand ML systems.

• Visualization aids in making informed decisions and effectively communicating results.

Types of Machine Learning Visualization:

• Model structure visualization helps data scientists, AI researchers, and business stakeholders understand complex algorithms and data flows.

• Model performance visualizations provide insights into the performance characteristics of individual models and model ensembles.

• Comparative model analysis visualizations help practitioners select the best-performing model or validate whether a new model version is an improvement.

• Feature importance visualizations reveal the impact of each input feature on model outputs.

Practices for Model Visualization:

• Start with clear objectives and simple visualizations.

• Select appropriate visualization methods that suit your needs and can be used by your target audience.

• Choose the right tools and libraries that help you create accurate visualizations efficiently.

• Continuously listen for feedback and adjust visualizations based on stakeholder needs.