A Deep Learning Dream: Accuracy and Interpretability in a Single Model

Machine learning is a discipline full of frictions and tradeoffs but none more important like the balance between accuracy and interpretability. In principle, highly accurate machine learning models such as deep neural networks tend to be really hard to interpret while simpler models like decision trees fall short in many sophisticated scenarios. Conventional machine learning wisdom tell us that accuracy and interpretability are opposite forces in the architecture of a model but its that always the case? Can we build models that are both highly performant and simple to understand? An interesting answer can be found in a paper published by researchers from IBM that proposes a statistical method for improving the performance of simpler machine learning models using the knowledge from more sophisticated models.

Finding the right balance between performance and interpretability in machine learning models is far from being a trivial endeavor. Psychologically, we are more attracted towards things we can explain while the homo- economicus inside us prefers the best outcome for a given problem. Many real world data science scenarios can be solved using both simple and highly sophisticated machine learning models. In those scenarios, the advantages of simplicity and interpretability tend to outweigh the benefits of performance.

#2020 sep tutorials # overviews #accuracy #deep learning #interpretability

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Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time

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Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.

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A Deep Learning Dream: Accuracy and Interpretability in a Single Model

Machine learning is a discipline full of frictions and tradeoffs but none more important like the balance between accuracy and interpretability. In principle, highly accurate machine learning models such as deep neural networks tend to be really hard to interpret while simpler models like decision trees fall short in many sophisticated scenarios. Conventional machine learning wisdom tell us that accuracy and interpretability are opposite forces in the architecture of a model but its that always the case? Can we build models that are both highly performant and simple to understand? An interesting answer can be found in a paper published by researchers from IBM that proposes a statistical method for improving the performance of simpler machine learning models using the knowledge from more sophisticated models.

Finding the right balance between performance and interpretability in machine learning models is far from being a trivial endeavor. Psychologically, we are more attracted towards things we can explain while the homo- economicus inside us prefers the best outcome for a given problem. Many real world data science scenarios can be solved using both simple and highly sophisticated machine learning models. In those scenarios, the advantages of simplicity and interpretability tend to outweigh the benefits of performance.

#2020 sep tutorials # overviews #accuracy #deep learning #interpretability

Uncovering the Magic: interpreting Machine Learning black-box models

**The trade-off between predictive power and interpretability **is a common issue to face when working with black-box models, especially in business environments where results have to be explained to non-technical audiences. Interpretability is crucial to being able to question, understand, and trust AI and ML systems. It also provides data scientists and engineers better means for debugging models and ensuring that they are working as intended.

Motivation

This tutorial aims to present different techniques for approaching model interpretation in black-box models.

_Disclaimer: _this article seeks to introduce some useful techniques from the field of interpretable machine learning to the average data scientists and to motivate its adoption . Most of them have been summarized from this highly recommendable book from Christoph Molnar: Interpretable Machine Learning.

The entire code used in this article can be found in my GitHub

Contents

1. Taxonomy of Interpretability Methods
2. Dataset and Model Training
3. Global Importance
4. Local Importance

1. Taxonomy of Interpretability Methods

• **Intrinsic or Post-Hoc? **This criteria distinguishes whether interpretability is achieved by restricting the complexity of the machine learning model (intrinsic) or by applying methods that analyze the model after training (post-hoc).
• **Model-Specific or Model-Agnostic? **Linear models have a model-specific interpretation, since the interpretation of the regression weights are specific to that sort of models. Similarly, decision trees splits have their own specific interpretation. Model-agnostic tools, on the other hand, can be used on any machine learning model and are applied after the model has been trained (post-hoc).
• **Local or Global? **Local interpretability refers to explaining an individual prediction, whereas global interpretability is related to explaining the model general behavior in the prediction task. Both types of interpretations are important and there are different tools for addressing each of them.

2. Dataset and Model Training

The dataset used for this article is the Adult Census Income from UCI Machine Learning Repository. The prediction task is to determine whether a person makes over $50K a year.

Since the focus of this article is not centered in the modelling phase of the ML pipeline, minimum feature engineering was performed in order to model the data with an XGBoost.

The performance metrics obtained for the model are the following:

Fig. 1: Receiving Operating Characteristic (ROC) curves for Train and Test sets.

Fig. 2: XGBoost performance metrics

The model’s performance seems to be pretty acceptable.

3. Global Importance

The techniques used to evaluate the global behavior of the model will be:

3.1 - Feature Importance (evaluated by the XGBoost model and by SHAP)

3.2 - Summary Plot (SHAP)

3.3 - Permutation Importance (ELI5)

3.4 - Partial Dependence Plot (PDPBox and SHAP)

3.5 - Global Surrogate Model (Decision Tree and Logistic Regression)

3.1 - Feature Importance

• XGBoost (model-specific)

feat_importances = pd.Series(clf_xgb_df.feature_importances_, index=X_train.columns).sort_values(ascending=True)
feat_importances.tail(20).plot(kind='barh')

Fig. 3: XGBoost Feature Importance

When working with XGBoost, one must be careful when interpreting features importances, since the results might be misleading. This is because the model calculates several importance metrics, with different interpretations. It creates an importance matrix, which is a table with the first column including the names of all the features actually used in the boosted trees, and the other with the resulting ‘importance’ values calculated with different metrics (Gain, Cover, Frequence). A more thourough explanation of these can be found here.

The **Gain **is the most relevant attribute to interpret the relative importance (i.e. improvement in accuracy) of each feature.

• SHAP

In general, SHAP library is considered to be a model-agnostic tool for addressing interpretability (we will cover SHAP’s intuition in the Local Importance section). However, the library has a model-specific method for tree-based machine learning models such as decision trees, random forests and gradient boosted trees.

explainer = shap.TreeExplainer(clf_xgb_df)
shap_values = explainer.shap_values(X_test)

shap.summary_plot(shap_values, X_test, plot_type = 'bar')

Fig. 4: SHAP Feature Importance

The XGBoost feature importance was used to evaluate the relevance of the predictors in the model’s outputs for the Train dataset and the SHAP one to evaluate it for Test dataset, in order to assess if the most important features were similar in both approaches and sets.

It is observed that the most important variables of the model are maintained, although in different order of importance (age seems to take much more relevance in the test set by SHAP approach).

3.2 Summary Plot (SHAP)

The SHAP Summary Plot is a very interesting plot to evaluate the features of the model, since it provides more information than the traditional Feature Importance:

• Feature Importance: variables are sorted in descending order of importance.
• Impact on Prediction: the position on the horizontal axis indicates whether the values of the dataset instances for each feature have more or less impact on the output of the model.
• Original Value: the color indicates, for each feature, whether it is a high or low value (in the range of each of the feature).
• Correlation: the correlation of a feature with the model output can be analyzed by evaluating its color (its range of values) and the impact on the horizontal axis. For example, it is observed that the age has a positive correlation with the target, since the impact on the output increases as the value of the feature increases.

shap.summary_plot(shap_values, X_test)

Fig. 5: SHAP Summary Plot

3.3 - Permutation Importance (ELI5)

Another way to assess the global importance of the predictors is to randomly permute the order of the instances for each feature in the dataset and predict with the trained model. If by doing this disturbance in the order, the evaluation metric does not change substantially, then the feature is not so relevant. If instead the evaluation metric is affected, then the feature is considered important in the model. This process is done individually for each feature.

To evaluate the trained XGBoost model, the Area Under the Curve (AUC) of the ROC Curve will be used as the performance metric. Permutation Importance will be analyzed in both Train and Test:

# Train
perm = PermutationImportance(clf_xgb_df, scoring = 'roc_auc', random_state=1984).fit(X_train, y_train)
eli5.show_weights(perm, feature_names = X_train.columns.tolist())

# Test
perm = PermutationImportance(clf_xgb_df, scoring = 'roc_auc', random_state=1984).fit(X_test, y_test)
eli5.show_weights(perm, feature_names = X_test.columns.tolist())

Fig. 6: Permutation Importance for Train and Test sets.

Even though the order of the most important features changes, it looks like that the most relevant ones remain the same. It is interesting to note that, unlike the XGBoost Feature Importance, the age variable in the Train set has a fairly strong effect (as showed by SHAP Feature Importance in the Test set). Furthermore, the 6 most important variables according to the Permutation Importance are kept in Train and Test (the difference in order may be due to the distribution of each sample).

The coherence between the different approaches to approximate the global importance generates more confidence in the interpretation of the model’s output.

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Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

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