49. What are some of the important parameters to consider when implementing a random forest algorithm in TensorFlow? There are six main parameters you should think about and plan when implementing arandom forest algorithmin TensorFlow: Number of inputs Feature count Number of samples per batch Tota...
Low bias: SVM, decision trees, KNN algorithm, etc. High bias: Linear and logistic regression 63. What is the use of Hash tables in statistics? Hash tables are the data structures that are used to denote the representation of key-value pairs in a structured way. The hashing function is us...
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Random Forest is a popular example of a bagging algorithm, employing decision trees. Key Mechanics Bootstrap Aggregating: Uses resampling, or "bootstrapping," to create multiple datasets for training. Parallel Model Building: Each dataset is used to train a separate model simultaneously. Code ...
12. What is a random forest? A random forest is an ensemble learning method that combines multiple decision trees to improve predictive accuracy and reduce overfitting. 13. What is gradient descent? Gradient descent is an optimization algorithm used to minimize the loss function in machine learning...
Unfortunately, machine learning algorithms tend to be “black boxes”; they work, but nobody can really say what the algorithm is doing. It’s a little like a chef “just knowing” when to take the cake out of the oven beacuse they’ve had lots of experience. In that sense there’s ...
We confirmed the accuracy of this approach to white blood cell identification using a random forest classifier algorithm on a data set of 3600 cells that we had previously categorized. Our approach has the potential to standardize white blood cell morphology training in undergraduate medical laboratory...
It depends a lot on the data one is dealing with and the initial values of the learning parameter. 33. What is the difference between Supervised and Unsupervised Learning? Supervised Learning Unsupervised Learning If an algorithm learns something from the training data so that the knowledge can ...
Naive Bayes (NB) is a supervised learning algorithm based on applying Bayes' theorem It is called naive because it builds the naive assumption that each feature are independent of each other NB can make different assumptions (i.e., data distributions, such as Gaussian, Multinomial, Bernoulli) ...
The random forest chooses the decision of the majority of the trees as the final decision. 24. Considering a Long List of Machine Learning Algorithms, given a Data Set, How Do You Decide Which One to Use? There is no master algorithm for all situations. Choosing an algorithm depends on ...