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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["keras","metrics","regression-metrics","scikit-learn","tensorflow2"],"created_at":"2024-11-19T04:10:07.437Z","updated_at":"2026-02-27T07:33:25.281Z","avatar_url":"https://github.com/ashishpatel26.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Regression Metrics\n\n## Installation\n\nTo install the package from the PyPi repository you can execute the following\ncommand:\n```bash\npip install regressionmetrics\n```\nIf you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:\n```bash\ngit clone https://github.com/ashishpatel26/regressionmetrics.git\ncd regressionmetrics\npip install .\n```\n\n| Metrics                     | Full Form                                      | Interpretation                      | Sklearn | Keras |\n| --------------------------- | ---------------------------------------------- | ----------------------------------- | ------- | ----- |\n| **MeanAbsoErr**                     | Mean Absolute Error                            | Smaller is better (Best value is 0) | ☑️       | ☑️     |\n| **MeanSqrtErr**                     | Mean Sqaured Error                             | Smaller is better(Best value is 0)  | ☑️       | ☑️     |\n| **RootMeanSqrtErr**                    | Root Mean Square Error                         | Smaller is better(Best value is 0)  | ☑️       | ☑️     |\n| **RootMeanSqrtLogErr**                   | Root Mean Square Log Error                     | Smaller is better(Best value is 0)  | ☑️       | ☑️     |\n| **RootMeanSqrtLogErrNeg**       | Root Mean Square Log Error with neg. value     | Smaller is better(Best value is 0)  | ☑️       |       |\n| **R2CoefScore**                | coefficient of determination                   | Best possible score is 1            | ☑️       | ☑️     |\n| **AdjR2CoefScore**       | Adjusted R2 score                              | Best possible score is 1            | ☑️       | ☑️     |\n| **MeanAbsPercErr**                    | Mean Absolute Percentage Error                 | Smaller is better(Best value is 0)  | ☑️       | ☑️     |\n| **MeanSqrtLogErr**                    | Mean Sqaured Logarithm Error                   | Smaller is better(Best value is 0)  | ☑️       | ☑️     |\n| **SymMeanAbsPercErr**                   | Symmetric mean absolute percentage error       | Smaller is better(Best value is 0)  | ☑️       |       |\n| **NormRootMeanSqrtErr**                   | Normalized Root Mean Square Error.             |                                     | ☑️       | ☑️     |\n| **NormRootMeanSqrtLogErr**                  | Normalized Root Mean Squared Logarithmic Error |                                     | ☑️       |       |\n| **MedianAbsErr**                | Median Absolute Error                          | Smaller is better(Best value is 0)  | ☑️       |       |\n| **MediaRelErr**                     | Mean Relative Error                            | Smaller is better(Best value is 0)  | ☑️       |       |\n| **MeanArcAbsPercErr**                   | Mean Arctangent Absolute Percentage Error      | Smaller is better(Best value is 0)  | ☑️       |       |\n| **NashSutCoeff**                     | Nash-Sutcliffe Efficiency Coefficient          | Larger is better (Best = 1)         | ☑️       |       |\n| **WillMottIndexAgreeMent** | Willmott Index                                 | Larger is better (Best = 1)         | ☑️       |       |\n\n* Mean Absolute Error - `sklearn, keras`\n\n* Mean Square Error - `sklearn, keras`\n* Root Mean Square Error - `sklearn, keras`\n* Root Mean Square Logarithmic Error - `sklearn, keras`\n* Root Mean Square Logarithmic Error with negative value handle - `sklearn`\n* R2 Score - `sklearn, keras`\n* Adjusted R2 Score - `sklearn, keras`\n* Mean Absolute Percentage Error - `sklearn, keras`\n* Mean squared logarithmic Error - `sklearn, keras`\n* Symmetric mean absolute percentage error - `sklearn, keras`\n* Normalized Root Mean Squared Error - `sklearn, keras`\n\nColaboratory File :  [![Open In Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/github/ashishpatel26/regressionmetrics/blob/main/RegressionMetricsDemo.ipynb)\n\n## Usage\n\n\u003e **Usage with scikit learn :**\n\n```python\nfrom regressionmetrics.metrics import *\ny_true = [3, 0.5, 2, 7]\ny_pred = [2.5, 0.0, 2, -8]\n\nprint(\"R2Score: \",R2CoefScore(y_true, y_pred))\nprint(\"Adjusted_R2_Score:\",AdjR2CoefScore(y_true, y_pred))\nprint(\"RMSE:\", RootMeanSqrtErr(y_true, y_pred))\nprint(\"MAE:\",MeanAbsoErr(y_true, y_pred))\nprint(\"RMSLE with Neg Value:\", RootMeanSqrtLogErrNeg(y_true, y_pred))\nprint(\"MSE:\", MeanSqrtErr(y_true, y_pred))\nprint(\"MAPE: \", MeanAbsPercErr(y_true, y_pred))\n```\n**Output:**\n\n```bash\nR2Score:  -8.725067385444744\nAdjusted_R2_Score: 20.450134770889484\nRMSE: 7.508328708840604\nMAE: 4.0\nRMSLE with Neg Value: 0.21344354447336292\nMSE: 56.375\nMAPE:  0.8273809523809523\n```\n\n\u003e **Usage with TensorFlow keras:**\n\n```python\ntry:\n  from regressionmetrics.keras import *\nexcept:\n  import os\n  os.system(\"pip install regressionmetrics\")\n\nfrom regressionmetrics.keras import *\nimport pandas as pd\nimport numpy as np\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n(x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data(path=\"boston_housing.npz\", test_split=0.2, seed=113)\n\nmodel = keras.Sequential([\n    layers.Dense(64, activation='relu', input_shape=(x_train.shape[1],)),\n    layers.Dense(64, activation='relu'),\n    layers.Dense(1)\n])\nmodel.compile(optimizer='rmsprop', loss='mse', metrics=[R2CoefScore, \n                                                        MeanAbsoErr, \n                                                        MeanSqrtErr, \n                                                        RootMeanSqrtErr, \n                                                        MeanAbsPercErr, \n                                                        RootMeanSqrtLogErr, \n                                                        NormRootMeanSqrtErr])\nmodel.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))\n```\n**Output**\n\n```bash\nEpoch 1/10\n13/13 [==============================] - 2s 29ms/step - loss: 461.6622 - R2CoefScore: 0.9004 - MeanAbsoErr: 14.6653 - MeanSqrtErr: 461.6622 - RootMeanSqrtErr: 14.6653 - MeanAbsPercErr: 75.2677 - RootMeanSqrtLogErr: 0.7278 - NormRootMeanSqrtErr: 0.7527 - val_loss: 300.4463 - val_R2CoefScore: 0.8947 - val_MeanAbsoErr: 15.4050 - val_MeanSqrtErr: 300.4463 - val_RootMeanSqrtErr: 15.4050 - val_MeanAbsPercErr: 69.2703 - val_RootMeanSqrtLogErr: 1.2662 - val_NormRootMeanSqrtErr: 0.6927\nEpoch 2/10\n13/13 [==============================] - 0s 4ms/step - loss: 184.7860 - R2CoefScore: 0.9527 - MeanAbsoErr: 10.9894 - MeanSqrtErr: 184.7860 - RootMeanSqrtErr: 10.9894 - MeanAbsPercErr: 56.5819 - RootMeanSqrtLogErr: 0.5995 - NormRootMeanSqrtErr: 0.5658 - val_loss: 305.9124 - val_R2CoefScore: 0.8910 - val_MeanAbsoErr: 15.4291 - val_MeanSqrtErr: 305.9124 - val_RootMeanSqrtErr: 15.4291 - val_MeanAbsPercErr: 71.9620 - val_RootMeanSqrtLogErr: 1.3943 - val_NormRootMeanSqrtErr: 0.7196\nEpoch 3/10\n13/13 [==============================] - 0s 5ms/step - loss: 198.5649 - R2CoefScore: 0.9507 - MeanAbsoErr: 12.0198 - MeanSqrtErr: 198.5649 - RootMeanSqrtErr: 12.0198 - MeanAbsPercErr: 62.6733 - RootMeanSqrtLogErr: 0.6901 - NormRootMeanSqrtErr: 0.6267 - val_loss: 80.2263 - val_R2CoefScore: 0.9807 - val_MeanAbsoErr: 7.0446 - val_MeanSqrtErr: 80.2263 - val_RootMeanSqrtErr: 7.0446 - val_MeanAbsPercErr: 43.2890 - val_RootMeanSqrtLogErr: 0.3114 - val_NormRootMeanSqrtErr: 0.4329\nEpoch 4/10\n13/13 [==============================] - 0s 6ms/step - loss: 197.9205 - R2CoefScore: 0.9613 - MeanAbsoErr: 10.8593 - MeanSqrtErr: 197.9205 - RootMeanSqrtErr: 10.8593 - MeanAbsPercErr: 56.8981 - RootMeanSqrtLogErr: 0.5830 - NormRootMeanSqrtErr: 0.5690 - val_loss: 139.6424 - val_R2CoefScore: 0.9512 - val_MeanAbsoErr: 9.2244 - val_MeanSqrtErr: 139.6424 - val_RootMeanSqrtErr: 9.2244 - val_MeanAbsPercErr: 38.9547 - val_RootMeanSqrtLogErr: 0.5582 - val_NormRootMeanSqrtErr: 0.3895\nEpoch 5/10\n13/13 [==============================] - 0s 4ms/step - loss: 164.3372 - R2CoefScore: 0.9641 - MeanAbsoErr: 10.6009 - MeanSqrtErr: 164.3372 - RootMeanSqrtErr: 10.6009 - MeanAbsPercErr: 55.5600 - RootMeanSqrtLogErr: 0.5740 - NormRootMeanSqrtErr: 0.5556 - val_loss: 142.1380 - val_R2CoefScore: 0.9564 - val_MeanAbsoErr: 10.7172 - val_MeanSqrtErr: 142.1380 - val_RootMeanSqrtErr: 10.7172 - val_MeanAbsPercErr: 63.0724 - val_RootMeanSqrtLogErr: 0.4243 - val_NormRootMeanSqrtErr: 0.6307\nEpoch 6/10\n13/13 [==============================] - 0s 5ms/step - loss: 176.5649 - R2CoefScore: 0.9584 - MeanAbsoErr: 11.0135 - MeanSqrtErr: 176.5649 - RootMeanSqrtErr: 11.0135 - MeanAbsPercErr: 56.6267 - RootMeanSqrtLogErr: 0.5719 - NormRootMeanSqrtErr: 0.5663 - val_loss: 217.2575 - val_R2CoefScore: 0.9235 - val_MeanAbsoErr: 12.4566 - val_MeanSqrtErr: 217.2575 - val_RootMeanSqrtErr: 12.4566 - val_MeanAbsPercErr: 55.4557 - val_RootMeanSqrtLogErr: 0.9559 - val_NormRootMeanSqrtErr: 0.5546\nEpoch 7/10\n13/13 [==============================] - 0s 4ms/step - loss: 157.5359 - R2CoefScore: 0.9567 - MeanAbsoErr: 9.5872 - MeanSqrtErr: 157.5359 - RootMeanSqrtErr: 9.5872 - MeanAbsPercErr: 50.5483 - RootMeanSqrtLogErr: 0.5250 - NormRootMeanSqrtErr: 0.5055 - val_loss: 411.2795 - val_R2CoefScore: 0.8542 - val_MeanAbsoErr: 18.6303 - val_MeanSqrtErr: 411.2795 - val_RootMeanSqrtErr: 18.6303 - val_MeanAbsPercErr: 85.9467 - val_RootMeanSqrtLogErr: 1.6382 - val_NormRootMeanSqrtErr: 0.8595\nEpoch 8/10\n13/13 [==============================] - 0s 4ms/step - loss: 115.8139 - R2CoefScore: 0.9795 - MeanAbsoErr: 7.9076 - MeanSqrtErr: 115.8139 - RootMeanSqrtErr: 7.9076 - MeanAbsPercErr: 39.5189 - RootMeanSqrtLogErr: 0.3936 - NormRootMeanSqrtErr: 0.3952 - val_loss: 72.1911 - val_R2CoefScore: 0.9813 - val_MeanAbsoErr: 6.7830 - val_MeanSqrtErr: 72.1911 - val_RootMeanSqrtErr: 6.7830 - val_MeanAbsPercErr: 40.6487 - val_RootMeanSqrtLogErr: 0.2993 - val_NormRootMeanSqrtErr: 0.4065\nEpoch 9/10\n13/13 [==============================] - 0s 5ms/step - loss: 214.5103 - R2CoefScore: 0.9397 - MeanAbsoErr: 10.9144 - MeanSqrtErr: 214.5103 - RootMeanSqrtErr: 10.9144 - MeanAbsPercErr: 56.2217 - RootMeanSqrtLogErr: 0.5520 - NormRootMeanSqrtErr: 0.5622 - val_loss: 87.2555 - val_R2CoefScore: 0.9733 - val_MeanAbsoErr: 6.8626 - val_MeanSqrtErr: 87.2555 - val_RootMeanSqrtErr: 6.8626 - val_MeanAbsPercErr: 28.4989 - val_RootMeanSqrtLogErr: 0.3236 - val_NormRootMeanSqrtErr: 0.2850\nEpoch 10/10\n13/13 [==============================] - 0s 6ms/step - loss: 159.1116 - R2CoefScore: 0.9662 - MeanAbsoErr: 9.1501 - MeanSqrtErr: 159.1116 - RootMeanSqrtErr: 9.1501 - MeanAbsPercErr: 46.7719 - RootMeanSqrtLogErr: 0.5018 - NormRootMeanSqrtErr: 0.4677 - val_loss: 69.8977 - val_R2CoefScore: 0.9841 - val_MeanAbsoErr: 6.0780 - val_MeanSqrtErr: 69.8977 - val_RootMeanSqrtErr: 6.0780 - val_MeanAbsPercErr: 32.4612 - val_RootMeanSqrtLogErr: 0.2741 - val_NormRootMeanSqrtErr: 0.3246\n\u003ckeras.callbacks.History at 0x7f78e997f550\u003e\n```\n\n:smiley: Thanks for reading and forking.\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishpatel26%2Fregressionmetrics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashishpatel26%2Fregressionmetrics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishpatel26%2Fregressionmetrics/lists"}