Random Forest Regressor.ipynb

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    "Q1. What is Random Forest Regressor?\n",
    "\n",
    "Ans.\n",
    "Random Forest Regressor is an ensemble learning method that consists of a collection of decision trees. Each decision tree is trained on a random subset of the training data and a random subset of the features. The predictions of the individual decision trees are then aggregated to produce a final prediction."
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    "Q2. How does Random Forest Regressor reduce the risk of overfitting?\n",
    "\n",
    "Ans.\n",
    "Random Forest Regressor reduces the risk of overfitting by training multiple decision trees on different subsets of the data. This helps to prevent the model from becoming too specialized to the training data and allows it to generalize better to new data."
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    "Q3. How does Random Forest Regressor aggregate the predictions of multiple decision trees?\n",
    "\n",
    "Ans.\n",
    "A Random Forest Regressor aggregates predictions of multiple decision trees by averaging their individual predictions for regression tasks. For each tree, predictions are made independently, and the final prediction is the average of these tree predictions, which helps reduce variance and improve accuracy."
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    "Q4. What are the hyperparameters of Random Forest Regressor?\n",
    "\n",
    "Ans.\n",
    "The hyperparameters of Random Forest Regressor are:\n",
    "\n",
    "n_estimators: The number of decision trees in the forest.\n",
    "max_depth: The maximum depth of each decision tree.\n",
    "min_samples_split: The minimum number of samples required to split a node in a decision tree.\n",
    "min_samples_leaf: The minimum number of samples required in a leaf node of a decision tree.\n",
    "max_features: The number of features to consider when looking for the best split at each node.\n",
    "bootstrap: Whether to use bootstrap sampling when training the decision trees.\n",
    "oob_score: Whether to calculate the out-of-bag error score for the model."
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    "Q5. What is the difference between Random Forest Regressor and Decision Tree Regressor?\n",
    "\n",
    "Ans.\n",
    "The main difference between Random Forest Regressor and Decision Tree Regressor is that Random Forest Regressor uses multiple decision trees to make predictions, while Decision Tree Regressor only uses a single decision tree. This makes Random Forest Regressor more robust to overfitting and less sensitive to noise in the data."
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    "Q6. What are the advantages and disadvantages of Random Forest Regressor?\n",
    "\n",
    "Ans.\n",
    "The advantages of Random Forest Regressor include:\n",
    "\n",
    "It is a very accurate and robust machine learning algorithm.\n",
    "It is less sensitive to noise in the data than other machine learning algorithms.\n",
    "It can be used to handle both regression and classification problems.\n",
    "\n",
    "The disadvantages of Random Forest Regressor include:\n",
    "\n",
    "It can be computationally expensive to train.\n",
    "It can be difficult to interpret the results of the model"
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    "Q7. What is the output of Random Forest Regressor?\n",
    "\n",
    "Ans.\n",
    "The output of Random Forest Regressor is a predicted value for the target variable. "
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    "Q8. Can Random Forest Regressor be used for classification tasks?\n",
    "\n",
    "Ans.\n",
    "Yes, Random Forest Regressor can be used for classification tasks. In this case, the target variable is a categorical variable. The model will learn to predict the category of the target variable by looking at the values of the features."
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