Added the Ensemble in ML File

ensemble-methods-notebooks-master/LICENSE

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+MIT License
+
+Copyright (c) 2020 Gautam Kunapuli
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ensemble-methods-notebooks-master/README.md

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+# Jupyter notebooks for "Ensemble Methods for Machine Learning"
+
+This repository contains companion material: data, Python code and Jupyter notebooks for [Ensemble Methods for Machine Learning (Manning Publications)](https://www.manning.com/books/ensemble-methods-for-machine-learning). The code and notebooks are released under the [MIT license](https://github.com/gkunapuli/ensemble-methods-notebooks/blob/master/LICENSE).
+
+These notebooks primarily use Python 3.7, scikit-learn 0.21 and matplotlib 3.2.1, though other packages such as pandas, seaborn and Keras make guest appearances as well.
+
+This book is a work in progress and expected to be released some time in early 2021.
+
+## List of notebooks
+
+* Chapter 1. Ensemble Methods: Hype or Halleujah?
+    * [1.3. Fit vs. Complexity in Machine-Learning Models](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch1.3-fit-vs-complexity.ipynb?flush_cache=true)
+    * [1.4. A Simple Model Averaging Ensemble](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch1.4-model-averaging-example.ipynb?flush_cache=True)
+
+* Chapter 2. Homogeneous Parallel Ensembles: Bagging and Random Forests
+   * [2.2, 2.3. Bagging and Random Forest](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch2.2and2.3-bagging-and-random-forest.ipynb?flush_cache=True)
+   * [2.5. Case Study: Breast Cancer Diagnosis](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch2.5-case-study-breast-cancer-diagnosis.ipynb?flush_cache=True)
+
+* Chapter 3: Heterogeneous Parallel Ensembles: Combining Strong Learners
+   * [3.1 Base Estimators for Heterogeneous Ensembles](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch3.1-base-estimators-for-heterogeneous-ensembles.ipynb?flush_cache=True)
+   * [3.2 Combining Predictions by Weighting](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch3.2-combining-predictions-by-weighting.ipynb?flush_cache=True)
+   * [3.3 Combining Predictions by Meta-Learning](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch3.3-combining-predictions-by-meta-learning.ipynb?flush_cache=True)
+   * [3.4 Case Study: Sentiment Analysis](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch3.4-case-study-sentiment-analysis.ipynb?flush_cache=True)
+
+
+* Chapter 4: Sequential Ensembles: Boosting
+   * [4.1, 4.2 AdaBoost: Adaptive Boosting](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch4.1and4.2-sequential-ensembles-and-Adaboost.ipynb?flush_cache=True)
+   * [4.3 AdaBoost in Practice](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch4.3-AdaBoost-in-practice.ipynb?flush_cache=True)
+   * [4.4 Case Study: Handwritten Digit Classification](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch4.4-case-study-handwritten-digit-classification.ipynb?flush_cache=True)
+   * [4.5 LogitBoost: Boosting with the Logistic Loss](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch4.5-LogitBoost-boosting-with-the-logistic-loss.ipynb?flush_cache=True)
+
+* Chapter 5: Sequential Ensembles: Gradient Boosting
+   * [5.1 Gradient Descent for Minimization](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch5.1-gradient-descent-for-minimization.ipynb?flush_cache=True)
+   * [5.2 Gradient Boosting = Gradient Descent + Boosting](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch5.2-gradient-boosting.ipynb?flush_cache=True)
+   * [5.3, 5.4 Practical Boosting with LightGBM](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch5.3and5.4-practical-boosting-with-lightgbm.ipynb?flush_cache=True)
+   * [5.5 Case Study: Document Retrieval](https://nbviewer.jupyter.org/github/gkunapuli/ensemble-methods-notebooks/blob/master/Ch5.5-case-study-document-retrieval.ipynb?flush_cache=True)
+
+* Chapter 6: Sequential Ensembles: Newton Boosting