Multi-Class Tabular Classification Model for Avila Bible Identification Using Python and TensorFlow Decision Forests

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Avila Bible Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The Avila dataset includes 800 images extracted from the “Avila Bible,” a giant Latin copy of the whole Bible produced during the XII century between Italy and Spain. The paleographic analysis of the manuscript has identified the presence of 12 transcribers; however, each transcriber did not transcribe the same number of pages. The prediction task is to associate each pattern to one of the 12 transcribers labeled as A, B, C, D, E, F, G, H, I, W, X, and Y. The research team normalized the data using the Z-normalization method and divided the dataset into two portions, training and test. The training set contains 10,430 samples, while the test set contains 10,437 samples.

ANALYSIS: The performance of the preliminary Gradient Boosted Trees model achieved an accuracy benchmark of 99.99% on the training dataset. When we applied the finalized model to the test dataset, the model achieved an accuracy score of 99.87%.

CONCLUSION: In this iteration, the TensorFlow Decision Forests model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Avila Bible Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/avila

One source of potential performance benchmarks: https://www.sciencedirect.com/science/article/abs/pii/S0952197618300721

The HTML formatted report can be found here on GitHub.

Multi-Class Tabular Classification Model for Avila Bible Identification Using Python and Scikit-Learn

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Avila Bible Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The Avila dataset includes 800 images extracted from the “Avila Bible,” a giant Latin copy of the whole Bible produced during the XII century between Italy and Spain. The paleographic analysis of the manuscript has identified the presence of 12 transcribers; however, each transcriber did not transcribe the same number of pages. The prediction task is to associate each pattern to one of the 12 transcribers labeled as A, B, C, D, E, F, G, H, I, W, X, and Y. The research team normalized the data using the Z-normalization method and divided the dataset into two portions, training and test. The training set contains 10,430 samples, while the test set contains 10,437 samples.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 85.51% using the training dataset. Furthermore, we selected Bagging Classifier as the final model as it processed the training dataset with a final accuracy score of 98.53%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.20%.

CONCLUSION: In this iteration, the Bagging Classifier model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Avila Bible Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/avila

One source of potential performance benchmarks: https://www.sciencedirect.com/science/article/abs/pii/S0952197618300721

The HTML formatted report can be found here on GitHub.

Roz Zander and Ben Zander on The Art of Possibility, Part 11

In the book, The Art of Possibility: Transforming Professional and Personal Life, Rosamund Stone Zander and Benjamin Zander show us the 12 things we can do to go on a journey of possibility, rather than living a life full of hurdles and constraints of our own making.

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

The Eleventh Practice: Creating Frameworks for Possibility

In this chapter, Roz and Ben discuss how we can invent and sustain frameworks that bring forth possibilities. They offer the following observations and recommendations for us to think about:

Roz and Ben suggest that there are three steps to the practice of framing possibility are:

  1. Make a new distinction in the realm of possibility: a powerful substitute for the current framework generating the downward spiral.
  2. Enter the territory. Embody the new distinction so that it becomes the framework for life around you.
  3. Keep distinguishing what is “on the track” and what is “off the track” of your framework for possibility.

When we distinguish our framework’s on-track vs. off-track notions, we focus on maintaining the framework’s clarity. Being “off-track” often tells us that the possibility we had defined is absent, forgotten, or has never been clearly articulated.

A vision is a powerful framework to take an organization’s operations from the downward spiral into the arena of possibility. However, a vision is not a mission statement, as they are not interchangeable.

Roz and Ben believe that a vision becomes a framework for possibility when it meets specific criteria.

  • A vision articulates a possibility.
  • A vision fulfills a desire fundamental to humankind, a passion with which any human being can resonate. Yet, it is an idea to which no one could logically respond, “What about me?”
  • A vision does not refer to morality or ethics; it is not about the right way of doing things. It cannot imply that anyone is wrong.
  • A vision is a picture of all time, using no numbers, measurements, or comparatives. It contains no specifics of time, place, audience, or product.
  • A vision is free-standing – it points neither to a rosier future nor a past in need of improvement. Instead, it gives over its bounty now.
  • A vision is a long line of possibilities radiating outward. It invites infinite expression, development, and proliferation within its definitional framework.
  • Speaking a vision transforms the speaker. For that moment, the “real world” becomes a universe of possibility and the barriers to realizing the vision disappear.

The practice of framing possibility calls upon us to use our minds to think about the contexts that govern us. It trains us to be alert to the danger that unseen definitions and assumptions may covertly chain us to a downward spiral.

We can define new frameworks for the possibility of bringing out the part of us that is most contributory, unencumbered, and most open to participation.

一項關於期望與令人驚訝的事情

(從我一個尊敬的作家,賽斯·高汀)

當您達到期望時,當您做出承諾並信守承諾時,當您的質量符合規範時,我們說“那當然”。

另一方面,如果你無情地提高期望,如果你過度承諾並增加一點炒作,你幾乎肯定無法實現我們的夢想和希望。然而與此同時,那些所提出的希望可能變成一種安慰劑,一種內在的認知失調,它會讓一些人更喜歡你的工作,而不希望看到你簡單地做更少承諾。

最後,如果您投入時間、精力和金錢來大幅超額交付,您今天的利潤可能不會那麼多,但這種不平衡通常可以通過對您有利的口耳相傳來彌補。當您的工作帶動驚訝和高興時,您的粉絲會樂意的付出代價。

進入我們的工業時代一百年後,每一種形式的期望都成為了它自己的信號。我們已經建立了對期望有的期望。如果你確切地告訴他們這些數字真正會是什麼樣子,你就無法從 VC 那裡籌集資金,而且如果外科醫生對所有細節都描述的一清二楚,那沒有人會進行任何手術。

這挑戰在於確保我們將正確的期望放在正確的類別中。

Binary-Class Tabular Classification Model for Raisin Grains Identification Using Python and TensorFlow

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The performance of the preliminary TensorFlow model achieved an accuracy benchmark of 86.05%. When we processed the test dataset with the final model, the model achieved an accuracy score of 91.11%.

CONCLUSION: In this iteration, the TensorFlow model appeared to be suitable for modeling this dataset.

Dataset Used: Raisin Dataset

Dataset ML Model: Binary classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.

Binary-Class Tabular Classification Model for Raisin Grains Identification Using Python and XGBoost

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 85.92%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 86.17%. When we processed the test dataset with the final model, the model achieved an accuracy score of 86.66%.

CONCLUSION: In this iteration, the XGBoost model appeared to be suitable for modeling this dataset.

Dataset Used: Raisin Dataset

Dataset ML Model: Binary classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.

Univariate Time Series Modeling Template Using TensorFlow Version 1

As I work on practicing and solving machine learning (ML) problems, I repeatedly re-use a programming set of steps and activities.

Thanks to Dr. Jason Brownlee’s suggestions on creating a machine learning template, I have pulled together a project template that can be used to support time series analysis using the TensorFlow framework and Python.

Version 1 of the TensorFlow time series template replicates many code segments within Dr. Brownlee’s blog post “Deep Learning Models for Univariate Time Series Forecasting”. The plan is to build a script for modeling future projects by adapting the example workflow presented in the blog.

The TensorFlow time series template is on the Analytics Project Templates page.

Binary-Class Tabular Classification Model for Raisin Grains Identification Using Python and TensorFlow Decision Forests

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

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The performance of the preliminary Random Forest model achieved an accuracy benchmark of 96.05% on the training dataset. When we applied the finalized model to the test dataset, the model achieved an accuracy score of 86.67%.

CONCLUSION: In this iteration, the TensorFlow Decision Forests model appeared to be suitable for modeling this dataset.

Dataset Used: Raisin Dataset

Dataset ML Model: Binary classification with numerical features

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.30855/gmbd.2020.03.03

The HTML formatted report can be found here on GitHub.