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Decision Tree Learning


Introduction to Machine Learning: Supervised Learning

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In this course, you'll be learning various supervised ML algorithms and prediction tasks applied to different data. You'll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.


Using regression techniques to predict a student's grade for a course

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I will be using Keras and TensorFlow to train a deep neural network to predict the grade using 2 hidden layers, mean squared error loss, and an RMSprop optimizer. Let's graph the error and the loss during training and evaluate the model We are getting a 0.69 mean absolute error with this approach. We also need to save the model to deploy it in an API. Since I am using google Colab I can easily save it to google drive. Initialize a random forest with 100 decision trees and train it on the same data.


GitHub - microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.

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Hummingbird compiles trained ML models into tensor computation for faster inference. - GitHub - microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.


Sr. AWS DevOps Developer with AI/ML experience

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Will accept BS in related field with minimum of 5 years of experience. A drive to learn and master new technologies and techniques. Excellent written and verbal communication skills for coordinating across teams. Green card or US citizen required. Will accept BS in related field with minimum of 5 years of experience.


Your ultimate AI/ML decision tree

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The services that will work best for you will depend on your specific use case and your team's level of expertise. Because it takes a lot of effort and ML expertise to build and maintain high quality ML models, a general rule of thumb is to use pretrained models or AI solutions whenever possible -- that is, when they fit your use case. If your data is structured, and it's in BigQuery, and your users are already comfortable with SQL, then choose BigQuery ML. If you realize that your use case requires writing your own model code, then use custom training options in Vertex AI. Let's look at your options in some more detail.


XGBoost Alternative Base Learners

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XGBoost, short for "Extreme Gradient Boosting," is one of the strongest machine learning algorithms for handling tabular data, a well-deserved reputation due to its success in winning numerous Kaggle competitions. XGBoost is an ensemble machine learning algorithm that usually consists of Decision Trees. The Decision Trees that make up XGBoost are individually referred to as gbtree, short for "gradient boosted tree." The first Decision Tree in the XGBoost ensemble is the base learner whose mistakes all subsequent trees learn from. Although Decision Trees are generally preferred as base learners due to their excellent ensemble scores, in some cases, alternative base learners may outperform them.


Decision Trees, Explained

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In this post we're going to discuss a commonly used machine learning model called decision tree. Decision trees are preferred for many applications, mainly due to their high explainability, but also due to the fact that they are relatively simple to set up and train, and the short time it takes to perform a prediction with a decision tree. Decision trees are natural to tabular data, and, in fact, they currently seem to outperform neural networks on that type of data (as opposed to images). Unlike neural networks, trees don't require input normalization, since their training is not based on gradient descent and they have very few parameters to optimize on. They can even train on data with missing values, but nowadays this practice is less recommended, and missing values are usually imputed.


How Random Forests & Decision Trees Decide: Simply Explained With An Example In Python

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Let's assume that we have a labeled dataset with 10 samples in total. What the Decision Trees do is simple: they find ways to split the data in a way such as that separate as much as possible the samples of the classes (increasing the class separability). In the above example, the perfect split would be a split at x 0.9 as this would lead to 5 red points being at the left side and the 5 blue at the right side (perfect class separability). Each time we split the space/data like that, we actually build a decision tree with a specific rule. Here we initially have the root node containing all the data and then, we split the data at x 0.9 leading to two branches leading to two leaf nodes.


Special Issue! Foundational Algorithms, Where They Came From, Where They're Going

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Years ago, I had to choose between a neural network and a decision tree learning algorithm. It was necessary to pick an efficient one, because we planned to apply the algorithm to a very large set of users on a limited compute budget. I went with a neural network. I hadn't used boosted decision trees in a while, and I thought they required more computation than they actually do -- so I made a bad call. Fortunately, my team quickly revised my decision, and the project was successful. This experience was a lesson in the importance of learning, and continually refreshing, foundational knowledge. If I had refreshed my familiarity with boosted trees, I would have made a better decision.


Machine Learning: Theory and Hands-on Practice with Python

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In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs.