Binary Classification Model for Truck APS Failure Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Truck APS Failure dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.

INTRODUCTION: The dataset consists of data collected from heavy Scania trucks in everyday usage. The system in focus is the Air Pressure system (APS), which generates pressurized air that supports functions such as braking and gear changes. The dataset’s positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS. The training set contains 60000 examples in total, in which 59000 belong to the negative class and 1000 positive class. The test set contains 16000 examples.

The challenge is to minimize the total cost of a prediction model the sum of “Cost_1” multiplied by the number of Instances with type 1 failure and “Cost_2” with the number of instances with type 2 failure. The “Cost_1” variable refers to the cost resulted from a redundant check by a mechanic at the workshop. Meanwhile, the “Cost_2” variable refers to the cost of not catching a faulty truck. The cost of Type I error (cost_1) is 10, while the cost of the Type II error (cost_2) is 500.

In the previous Scikit-Learn iterations, we constructed and tuned machine learning models for this dataset using the Scikit-Learn and the XGboost libraries. We also observed the best accuracy result that we could obtain using the tuned models with the training, validation, and test datasets.

In this Take1 iteration, we will construct and tune machine learning models for this dataset using TensorFlow with three layers. We will observe the best accuracy result that we can obtain using the tuned models with the validation and test datasets.

ANALYSIS: From the previous Scikit-Learn iterations, the optimized XGBoost model processed the testing dataset with a recall metric of 98.66% with a low Type II error rate.

From this Take1 iteration, the performance of the three-layer TensorFlow model achieved a recall score of 77.20% with the training dataset. After a series of tuning trials, the TensorFlow model processed the validation dataset with a recall score of 75.20%, which was consistent with the prediction from the training result. When configured with the optimized parameters, the TensorFlow model processed the test dataset with a recall score of 55.46% with a high Type II error rate.

CONCLUSION: For this dataset, the model built using TensorFlow did not achieve a satisfactory result, but we should consider using TensorFlow to model further.

Dataset Used: APS Failure at Scania Trucks Data Set

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.