Time Series Model Template Using Exponential Smoothing Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

The purpose of this modeling exercise is to construct an end-to-end template for solving machine learning problems. This Python script will adapt Dr. Jason Brownlee’s blog post on this topic and build a robust template for solving similar problems.

Version 1 of the exponential smoothing template contains structures and features that are like the ARIMA template. I pull together this template to take a machine learning exercise from beginning to end.

You will find the Python templates on the Machine Learning Project Templates page.

Regression Modeling Template Using Python and AutoKeras Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

The purpose of this modeling exercise is to construct an end-to-end template for solving machine learning problems. This Python script will adapt Dr. Jason Brownlee’s blog post on this topic and build a robust template for solving similar problems.

Version 1 of the AutoKeras regression template contains structures and features that are like the Scikit-Learn templates. I pull together this template to take a machine learning exercise from beginning to end.

You will find the Python templates on the Machine Learning Project Templates page.

Multi-Class Modeling Template Using Python and AutoKeras Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

The purpose of this modeling exercise is to construct an end-to-end template for solving machine learning problems. This Python script will adapt Dr. Jason Brownlee’s blog post on this topic and build a robust template for solving similar problems.

Version 1 of the AutoKeras multi-class template contains structures and features that are like the Scikit-Learn templates. I pull together this template to take a machine learning exercise from beginning to end.

You will find the Python templates on the Machine Learning Project Templates page.

Scott Adams on Loserthink, Part 9

In the book, Loserthink: How Untrained Brains Are Ruining America, Scott Adams analyzed and discussed ways to teach us how to eliminate our biases and to sharpen our ability to think critically.

These are some of my favorite quotes and takeaways from reading the book.

Here are tips on how we can help others break out of their mental prison.

What to watch out for: The Magic Question

“State ONE thing you believe on this topic that you think I do NOT believe.”

“Don’t play Whack-A-Mole with people who have laundry lists of reasons supporting their hallucinations. Ask for their strongest point only, and debunk it if you can. Target their undue confidence, not their entire laundry list.”

What to watch out for: Pacing

“Agree with people as much as you can without lying, and you will be in a better position to persuade.”

What to watch out for: Define the Weeds

“Don’t argue in the weeds of a debate. Dismiss the trivial stuff and concentrate on the variables that matter. That gives you the high ground.”

What to watch out for: Describe the Long Term

“Ask people with opposing opinions to describe what the future would look like if their view of the world were to play out. Does it sound reasonable?”

What to watch out for: Calling Out the Mind Reading

“The best way to avoid the mind reading illusion is to look for it in others. That will prime you to better catch yourself when you do your own mind reading.”

What to watch out for: Framing Issues

“You can’t get the right answer until you frame the question correctly. And partisans rarely do.”

Regression Model for Superconductor Critical Temperature Using Python and TensorFlow Take 4

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 Superconductor Critical Temperature dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: The research team wishes to create a statistical model for predicting the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The model seeks to examine the features that can contribute the most to the model’s predictive accuracy.

From iteration Take1, we constructed and tuned machine learning models for this dataset using TensorFlow with five layers. We also observed the best result that we could obtain using the tuned models with the validation and test datasets.

From iteration Take2, we constructed and tuned machine learning models for this dataset using TensorFlow with dropout layers. We also observed the best result that we could obtain using the tuned models with the validation and test datasets.

From iteration Take3, we constructed and tuned a TensorFlow model with five layers using the additional material attributes available for modeling. Furthermore, we also applied the tuned model to a test dataset and observed the best result that we could obtain from the model.

In this Take4 iteration, we will construct and tune a TensorFlow model with dropout layers using the additional material attributes available for modeling. Furthermore, we will apply the tuned model to a test dataset and observe the best result that we can obtain from the model.

ANALYSIS: From iteration Take1, the baseline performance of the TensorFlow algorithm achieved an RMSE benchmark of 11.109. After a series of tuning trials, the TensorFlow model processed the validation dataset with an RMSE score of 10.564. When we applied the TensorFlow model to the previously unseen test dataset, we obtained an RMSE score of 10.540.

From iteration Take2, the baseline performance of the TensorFlow algorithm achieved an RMSE benchmark of 10.580. After a series of tuning trials, the TensorFlow model processed the validation dataset with an RMSE score of 10.905. When we applied the TensorFlow model to the previously unseen test dataset, we obtained an RMSE score of 10.885.

From iteration Take3, the baseline performance of the TensorFlow algorithm achieved an RMSE benchmark of 12.298. After a series of tuning trials, the TensorFlow model processed the validation dataset with an RMSE score of 10.299. When we applied the TensorFlow model to the previously unseen test dataset, we obtained an RMSE score of 10.144.

In this Take4 iteration, the baseline performance of the TensorFlow algorithm achieved an RMSE benchmark of 10.304. After a series of tuning trials, the TensorFlow model processed the validation dataset with an RMSE score of 11.048. When we applied the TensorFlow model to the previously unseen test dataset, we obtained an RMSE score of 10.476.

CONCLUSION: In this iteration, the TensorFlow model with the dropout layers did not appear to have a noticeable effect on the modeling of this dataset. However, we still should consider using the algorithm for further modeling.

Dataset Used: Superconductivity Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Superconductivty+Data

One potential source of performance benchmarks: https://doi.org/10.1016/j.commatsci.2018.07.052

The HTML formatted report can be found here on GitHub.

Regression Model for Superconductor Critical Temperature Using Python and TensorFlow Take 3

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 Superconductor Critical Temperature dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: The research team wishes to create a statistical model for predicting the superconducting critical temperature based on the features extracted from the superconductor’s chemical formula. The model seeks to examine the features that can contribute the most to the model’s predictive accuracy.

From iteration Take1, we constructed and tuned machine learning models for this dataset using TensorFlow with five layers. We also observed the best result that we could obtain using the tuned models with the validation and test datasets.

From iteration Take2, we constructed and tuned machine learning models for this dataset using TensorFlow with dropout layers. We also observed the best result that we could obtain using the tuned models with the validation and test datasets.

In this Take3 iteration, we will construct and tune a TensorFlow model with five layers using the additional material attributes available for modeling. Furthermore, we will apply the tuned model to a test dataset and observe the best result that we can obtain from the model.

ANALYSIS: From iteration Take1, the baseline performance of the TensorFlow algorithm achieved an RMSE benchmark of 11.109. After a series of tuning trials, the TensorFlow model processed the validation dataset with an RMSE score of 10.564. When we applied the TensorFlow model to the previously unseen test dataset, we obtained an RMSE score of 10.540.

From iteration Take2, the baseline performance of the TensorFlow algorithm achieved an RMSE benchmark of 10.580. After a series of tuning trials, the TensorFlow model processed the validation dataset with an RMSE score of 10.905. When we applied the TensorFlow model to the previously unseen test dataset, we obtained an RMSE score of 10.885.

In this Take3 iteration, the baseline performance of the TensorFlow algorithm achieved an RMSE benchmark of 12.298. After a series of tuning trials, the TensorFlow model processed the validation dataset with an RMSE score of 10.299. When we applied the TensorFlow model to the previously unseen test dataset, we obtained an RMSE score of 10.144.

CONCLUSION: In this iteration, the TensorFlow model appeared to be a suitable algorithm for modeling this dataset. We should consider using the algorithm for further modeling.

Dataset Used: Superconductivity Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Superconductivty+Data

One potential source of performance benchmarks: https://doi.org/10.1016/j.commatsci.2018.07.052

The HTML formatted report can be found here on GitHub.

Binary Classification Modeling Template Using Python and AutoKeras Version 1

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

The purpose of this modeling exercise is to construct an end-to-end template for solving machine learning problems. This Python script will adapt Dr. Jason Brownlee’s blog post on this topic and build a robust template for solving similar problems.

Version 1 of the AutoKeras binary classification template contains structures and features that are like the Scikit-Learn templates. I pull together this template to take a machine learning exercise from beginning to end.

You will find the Python templates on the Machine Learning Project Templates page.