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Sto caricando le informazioni... Practical Machine Learning in R (edizione 2020)di Fred Nwanganga (Autore), Mike Chapple (Autore)
Informazioni sull'operaPractical Machine Learning in R di Fred Nwanganga
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Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning ?a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions ?allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field. Non sono state trovate descrizioni di biblioteche |
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So why not 5 stars?
So, I basically re-typed all the code from the book and I often ended up with different results especially for logistic regression and classifications. Sometimes it's to be expected but I used the exact same seed so, I should have gotten the same results. Part of the issue is that the dataset (which are provided with the book, you just have to download them from the website) are not exactly the same as they are in the book. Some had different numbers of rows.
The other bigger problem was that, in the classification problems, we often had to deal with class imbalances. To address that, the book uses a library that no longer exist (Torgo's DMwR). There is a DMwR2 library that goes with the 2nd edition of DMwR but it does not include the SMOTE function that is used throughout the book.
Now, it's not an impossible problem. There are other ways of addressing class imbalance. However, choosing a different method then guarantees different results. Mine were closed except for logistic regression.
That being said, if you teach that stuff to beginners, the book is worth it because of the clarity of the explanations. You can always fix the rest. But don't just assign the book without the proper corrections. ( )