random_forest_regression.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"random_forest_regression.ipynb","provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyP3S2RySFq2hXXtEz6ZoWRz"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"LeamvpPVXuS_","colab_type":"text"},"source":["# Random Forest Regression"]},{"cell_type":"markdown","metadata":{"id":"O2wvZ7SKXzVC","colab_type":"text"},"source":["## Importing the libraries"]},{"cell_type":"code","metadata":{"id":"PVmESEFZX4Ig","colab_type":"code","colab":{}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zgbK_F8-X7em","colab_type":"text"},"source":["## Importing the dataset"]},{"cell_type":"code","metadata":{"id":"adBE4tjQX_Bh","colab_type":"code","colab":{}},"source":["dataset = pd.read_csv('Position_Salaries.csv')\n","X = dataset.iloc[:, 1:-1].values\n","y = dataset.iloc[:, -1].values"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"v4S2fyIBYDcu","colab_type":"text"},"source":["## Training the Random Forest Regression model on the whole dataset"]},{"cell_type":"code","metadata":{"id":"o8dOCoJ1YKMc","colab_type":"code","outputId":"8894f460-d2de-44ab-d79b-9e69dffb12cb","executionInfo":{"status":"ok","timestamp":1587885572476,"user_tz":-240,"elapsed":2418,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":139}},"source":["from sklearn.ensemble import RandomForestRegressor\n","regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)\n","regressor.fit(X, y)"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n","                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n","                      max_samples=None, min_impurity_decrease=0.0,\n","                      min_impurity_split=None, min_samples_leaf=1,\n","                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n","                      n_estimators=10, n_jobs=None, oob_score=False,\n","                      random_state=0, verbose=0, warm_start=False)"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"8IbsXbK3YM4M","colab_type":"text"},"source":["## Predicting a new result"]},{"cell_type":"code","metadata":{"id":"pTXrS8FEYQlJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"6bfe8b5e-125c-43ce-ee32-d2f53477e0ee","executionInfo":{"status":"ok","timestamp":1587885584219,"user_tz":-240,"elapsed":1618,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}}},"source":["regressor.predict([[6.5]])"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([167000.])"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"kLqF9yMbYTon","colab_type":"text"},"source":["## Visualising the Random Forest Regression results (higher resolution)"]},{"cell_type":"code","metadata":{"id":"BMlTBifVYWNr","colab_type":"code","outputId":"71439b75-32b1-444b-a9ee-7ae0672cea82","executionInfo":{"status":"ok","timestamp":1587885590785,"user_tz":-240,"elapsed":1669,"user":{"displayName":"Hadelin de Ponteves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["X_grid = np.arange(min(X), max(X), 0.01)\n","X_grid = X_grid.reshape((len(X_grid), 1))\n","plt.scatter(X, y, color = 'red')\n","plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n","plt.title('Truth or Bluff (Random Forest Regression)')\n","plt.xlabel('Position level')\n","plt.ylabel('Salary')\n","plt.show()"],"execution_count":5,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}