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Model Parameters and Hyperparameters in Machine Learning -- What is the difference? -


For example, suppose you want to build a simple linear regression model using an m-dimensional training data set. For more information about this, see the following example: Machine Learning: Python Linear Regression Estimator Using Gradient Descent. Here, kernel specifies the kernel type to be used in the algorithm, for example kernel'linear', for linear classification, or kernel'rbf' for non-linear classification. Here, alpha is the regularization parameter. It is important that during model building, these hyperparameters be fine-tuned in order to obtain the model with the highest quality.

sklearn.dummy.DummyClassifier -- scikit-learn 0.21.3 documentation


Please cite us if you use the software. DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems. Strategy to use to generate predictions.

Expanding your machine learning toolkit: Randomized search, computational budgets, and new algorithms by Anonymous


Previously, we wrote about some common trade-offs in machine learning and the importance of tuning models to your specific dataset. We demonstrated how to tune a random forest classifier using grid search, and how cross-validation can help avoid overfitting when tuning hyperparameters (HPs). You'll learn a different strategy for traversing hyperparameter space - randomized search - and how to use it to tune two other classification algorithms - a support vector machine and a regularized logistic regression classifier. We'll keep working with the wine dataset, which contains chemical characteristics of wines of varying quality. As before, our goal is to try to predict a wine's quality from these features.

Understanding Boosted Trees Models


In the previous post, we learned about tree based learning methods - basics of tree based models and the use of bagging to reduce variance. We also looked at one of the most famous learning algorithms based on the idea of bagging- random forests. In this post, we will look into the details of yet another type of tree-based learning algorithms: boosted trees. Boosting, similar to Bagging, is a general class of learning algorithm where a set of weak learners are combined to get strong learners. For classification problems, a weak learner is defined to be a classifier which is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.

The Random Forest Algorithm – Towards Data Science


Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it's simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it. Random Forest is a supervised learning algorithm. Like you can already see from it's name, it creates a forest and makes it somehow random.