It is important to set its value appropriately to avoid overfitting. It is one of the most important hyperparameters when it comes to increasing the accuracy of the model, as we increase the depth of the tree the model accuracy increases upto a certain limit but then it will start to decrease gradually because of overfitting in the model. max_depth: It governs the maximum height upto which the trees inside the forest can grow.The default number of estimators is 100 in scikit-learn. However, it will not cause any overfitting but can certainly increase the time complexity of the model. We may think that using many trees to fit a model will help us to get a more generalized result, but this is not always the case. n_estimators: We know that a random forest is nothing but a group of many decision trees, the n_estimator parameter controls the number of trees inside the classifier.Therefore, we will be having a closer look at the hyperparameters of random forest classifier to have a better understanding of the inbuilt hyperparameters: On the other hand, not finding the optimal values of hyperparameters can also result in less accuracy because of overfitting issue. ML | One Hot Encoding to treat Categorical data parametersĪ deep understanding of hyperparameters is required because they are responsible for deciding how quickly a model can fit onto the data to produce accurate results.ML | Label Encoding of datasets in Python.Introduction to Hill Climbing | Artificial Intelligence.Best Python libraries for Machine Learning.Activation functions in Neural Networks.Elbow Method for optimal value of k in KMeans.Decision Tree Introduction with example.Linear Regression (Python Implementation).Removing stop words with NLTK in Python.
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