（從我一個尊敬的作家，賽斯·高汀）

簡單的問題往往是一種錯覺。 如果它們是真實的並且它們很容易處理，那麼它們已經被人解決了。

另一方面，困難的問題會一直存在，直到有洞察力、奉獻精神和承諾的人出現，並開始著手工作。

找出困難的問題會遠比去迴避它們來的有效得多。

Skip to content
# 困難的問題

# Multi-Class Model for Kaggle Tabular Playground Series February 2022 Using TensorFlow

# Multi-Class Model for Kaggle Tabular Playground Series February 2022 Using Decision Forests

# Univariate Time Series Model for USA Air Passenger Miles Using TensorFlow

# Multi-Class Model for Kaggle Tabular Playground Series February 2022 Using XGBoost

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

Professionals merely do work and ship art!

Month: March 2022

（從我一個尊敬的作家，賽斯·高汀）

簡單的問題往往是一種錯覺。 如果它們是真實的並且它們很容易處理，那麼它們已經被人解決了。

另一方面，困難的問題會一直存在，直到有洞察力、奉獻精神和承諾的人出現，並開始著手工作。

找出困難的問題會遠比去迴避它們來的有效得多。

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 Kaggle Tabular Playground Series February 2022 dataset is a multi-class modeling situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. For this dataset, we want to predict bacteria species based on repeated lossy measurements of DNA snippets. Each row of data contains a spectrum of histograms generated by repeated measurements of a sample, and each row contains the output of all 286 histogram possibilities.

ANALYSIS: The average performance of the preliminary TensorFlow models achieved an accuracy benchmark of 98.20%. When we processed the test dataset with the final model, the model achieved an accuracy score of 90.72%.

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

Dataset Used: Kaggle Tabular Playground Series February 2022

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2022/data

One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-feb-2022/leaderboard

The HTML formatted report can be found here on GitHub.

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 Kaggle Tabular Playground Series February 2022 dataset is a multi-class modeling situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. For this dataset, we want to predict bacteria species based on repeated lossy measurements of DNA snippets. Each row of data contains a spectrum of histograms generated by repeated measurements of a sample, and each row contains the output of all 286 histogram possibilities.

ANALYSIS: The performance of the preliminary Gradient Boosted Trees model achieved an accuracy benchmark of 99.36% on the training dataset. When we applied the finalized model to Kaggle’s test dataset, the model achieved an accuracy score of 91.55%.

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

Dataset Used: Kaggle Tabular Playground Series February 2022

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2022/data

One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-feb-2022/leaderboard

The HTML formatted report can be found here on GitHub.

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

SUMMARY: The project aims to construct a time series prediction model and document the end-to-end steps using a template. The USA Air Passenger Miles dataset is a univariate time series situation where we attempt to forecast future outcomes based on past data points.

ANALYSIS: The baseline persistence model yielded an RMSE of 1.581. The CNN-LSTM model processed the same test data with an RMSE of 1.109, which was better than the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (1, 1, 2) and seasonal order of (1, 1, 1, 12) processed the validation data with an RMSE of 0.437.

CONCLUSION: For this dataset, the TensorFlow CNN-LSTM model achieved an acceptable result, and we should consider using TensorFlow for further modeling.

Dataset Used: Monthly U.S Air Passenger Miles January 1960 through December 1977.

Dataset ML Model: Time series forecast with numerical attribute

Dataset Reference: Rob Hyndman and Yangzhuoran Yang (2018). tsdl: Time Series Data Library. v0.1.0. https://pkg.yangzhuoranyang./tsdl/.

The HTML formatted report can be found here on GitHub.

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 Kaggle Tabular Playground Series February 2022 dataset is a multi-class modeling situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. For this dataset, we want to predict bacteria species based on repeated lossy measurements of DNA snippets. Each row of data contains a spectrum of histograms generated by repeated measurements of a sample, and each row contains the output of all 286 histogram possibilities.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 98.16%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 99.24%. When we processed the test dataset with the final model, the model achieved an accuracy score of 93.45%.

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

Dataset Used: Kaggle Tabular Playground Series February 2022

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2022/data

One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-feb-2022/leaderboard

The HTML formatted report can be found here on GitHub.

You must be logged in to post a comment.