\"Log loss is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true. The log loss is only defined for two or more labels.\"
\nhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html
\nBy Max Kuhn
\n\"Log loss is a measure of the performance of a classification model. A perfect model has a log loss of 0.\"
\n\"Compared with accuracy(), log loss takes into account the uncertainty in the prediction and gives a more detailed view into the actual performance. For example, given two input probabilities of .6 and .9 where both are classified as predicting a positive value, say, \"Yes\", the accuracy metric would interpret them as having the same value. If the true output is \"Yes\", log loss penalizes .6 because it is \"less sure\" of it's result compared to the probability of .9.\"
\nMulticlass
\n\"Log loss has a known multiclass extension, and is simply the sum of the log loss values for each class prediction. Because of this, no averaging types are supported.\"
\nhttps://yardstick.tidymodels.org/reference/mn_log_loss.html
\nIntuition behind Log-loss score - By Gaurav Dembla - Nov 17, 2020
\n\"Where i is the given observation/record, y is the actual/true value, p is the prediction probability, and ln refers to the natural logarithm (logarithmic value using base of e) of a number.\"
\n\"Log-loss value is calculated for each observation based on observation’s actual value (y) and prediction probability (p). In order to evaluate a model and summarize its skill, log-loss score of the classification model is reported as average of log-losses of all the observations/predictions.\"
\n\"A model with perfect skill has a log-loss score of 0. In other words, the model predicts each observation’s probability as the actual value. \"
\n\"What log-loss score is to a classification problem, mean squared error (MSE) is to a regression problem. Both the metrics indicate how good or bad the prediction results are by denoting how far the predictions are from the actual values. \"
\n\"A model with lower log-loss score is better than the one with higher log-loss score, provided both the models are applied to the same distribution of dataset. We cannot compare log-loss scores of two models applied on two different datasets.\"
\n\"Baseline log-loss score for a dataset is determined from the naïve classification model, which simply pegs all the observations with a constant probability equal to % of data with class 1 observations. For a balanced dataset with a 51:49 ratio of class 0 to class 1, a naïve model with constant probability of 0.49 will yield log-loss score of 0.693, which is regarded as the baseline score for that dataset.\"
\n\"Higher the imbalance in a dataset, lower the baseline log-loss score of the dataset, due to lower proportion of observations that have higher impact on average of log-loss values.\"
\nhttps://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a
\nBinary Cross Entropy aka Log Loss-The cost function used in Logistic Regression
\nBy Megha Setia — November 9, 2020
What is Log Loss?
\n\"Log Loss is the most important classification metric based on probabilities. It’s hard to interpret raw log-loss values, but log-loss is still a good metric for comparing models. For any given problem, a lower log loss value means better predictions. Mathematical interpretation:\"
\"Log Loss is the negative average of the log of corrected predicted probabilities for each instance.\"
\n\"In short, there are three steps to find Log Loss: To find corrected probabilities; Take a log of corrected probabilities and Take the negative average of the values we get in the 2nd step.\"
\n\nBy Octosport.io - Nov 25, 2021
\n\n\"You will never do it wrong\" Octosport.io words.
\nI'll never do it wrong? That's because you don't know me. Error is my second name.😏
", "messages": [ { "id": 1721229, "postDate": "2022-03-13T14:22:49.783Z", "content": "\"Log loss is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true. The log loss is only defined for two or more labels.\"
\nhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html
\nBy Max Kuhn
\n\"Log loss is a measure of the performance of a classification model. A perfect model has a log loss of 0.\"
\n\"Compared with accuracy(), log loss takes into account the uncertainty in the prediction and gives a more detailed view into the actual performance. For example, given two input probabilities of .6 and .9 where both are classified as predicting a positive value, say, \"Yes\", the accuracy metric would interpret them as having the same value. If the true output is \"Yes\", log loss penalizes .6 because it is \"less sure\" of it's result compared to the probability of .9.\"
\nMulticlass
\n\"Log loss has a known multiclass extension, and is simply the sum of the log loss values for each class prediction. Because of this, no averaging types are supported.\"
\nhttps://yardstick.tidymodels.org/reference/mn_log_loss.html
\nIntuition behind Log-loss score - By Gaurav Dembla - Nov 17, 2020
\n\"Where i is the given observation/record, y is the actual/true value, p is the prediction probability, and ln refers to the natural logarithm (logarithmic value using base of e) of a number.\"
\n\"Log-loss value is calculated for each observation based on observation’s actual value (y) and prediction probability (p). In order to evaluate a model and summarize its skill, log-loss score of the classification model is reported as average of log-losses of all the observations/predictions.\"
\n\"A model with perfect skill has a log-loss score of 0. In other words, the model predicts each observation’s probability as the actual value. \"
\n\"What log-loss score is to a classification problem, mean squared error (MSE) is to a regression problem. Both the metrics indicate how good or bad the prediction results are by denoting how far the predictions are from the actual values. \"
\n\"A model with lower log-loss score is better than the one with higher log-loss score, provided both the models are applied to the same distribution of dataset. We cannot compare log-loss scores of two models applied on two different datasets.\"
\n\"Baseline log-loss score for a dataset is determined from the naïve classification model, which simply pegs all the observations with a constant probability equal to % of data with class 1 observations. For a balanced dataset with a 51:49 ratio of class 0 to class 1, a naïve model with constant probability of 0.49 will yield log-loss score of 0.693, which is regarded as the baseline score for that dataset.\"
\n\"Higher the imbalance in a dataset, lower the baseline log-loss score of the dataset, due to lower proportion of observations that have higher impact on average of log-loss values.\"
\nhttps://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a
\nBinary Cross Entropy aka Log Loss-The cost function used in Logistic Regression
\nBy Megha Setia — November 9, 2020
What is Log Loss?
\n\"Log Loss is the most important classification metric based on probabilities. It’s hard to interpret raw log-loss values, but log-loss is still a good metric for comparing models. For any given problem, a lower log loss value means better predictions. Mathematical interpretation:\"
\"Log Loss is the negative average of the log of corrected predicted probabilities for each instance.\"
\n\"In short, there are three steps to find Log Loss: To find corrected probabilities; Take a log of corrected probabilities and Take the negative average of the values we get in the 2nd step.\"
\n\nBy Octosport.io - Nov 25, 2021
\n\n\"You will never do it wrong\" Octosport.io words.
\nI'll never do it wrong? That's because you don't know me. Error is my second name.😏
", "rawMarkdown": "#Log Loss and Multinomial LR\n\n\"Log loss is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true. The log loss is only defined for two or more labels.\"\n\nhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html\n\n#Log Loss and Multiclass\nBy Max Kuhn\n\n\"Log loss is a measure of the performance of a classification model. A perfect model has a log loss of 0.\"\n\n\"Compared with accuracy(), log loss takes into account the uncertainty in the prediction and gives a more detailed view into the actual performance. For example, given two input probabilities of .6 and .9 where both are classified as predicting a positive value, say, \"Yes\", the accuracy metric would interpret them as having the same value. If the true output is \"Yes\", log loss penalizes .6 because it is \"less sure\" of it's result compared to the probability of .9.\"\n\nMulticlass\n\n\"Log loss has a known multiclass extension, and is simply the sum of the log loss values for each class prediction. Because of this, no averaging types are supported.\"\n\nhttps://yardstick.tidymodels.org/reference/mn_log_loss.html\n\n\n#How to calculate Log Loss\n\nIntuition behind Log-loss score - By Gaurav Dembla - Nov 17, 2020\n\n\"Where i is the given observation/record, y is the actual/true value, p is the prediction probability, and ln refers to the natural logarithm (logarithmic value using base of e) of a number.\"\n\n\"Log-loss value is calculated for each observation based on observation’s actual value (y) and prediction probability (p). In order to evaluate a model and summarize its skill, log-loss score of the classification model is reported as average of log-losses of all the observations/predictions.\"\n\n\"A model with perfect skill has a log-loss score of 0. In other words, the model predicts each observation’s probability as the actual value. \"\n\n\"What log-loss score is to a classification problem, mean squared error (MSE) is to a regression problem. Both the metrics indicate how good or bad the prediction results are by denoting how far the predictions are from the actual values. \"\n\n\"A model with lower log-loss score is better than the one with higher log-loss score, provided both the models are applied to the same distribution of dataset. We cannot compare log-loss scores of two models applied on two different datasets.\"\n\n\"Baseline log-loss score for a dataset is determined from the naïve classification model, which simply pegs all the observations with a constant probability equal to % of data with class 1 observations. For a balanced dataset with a 51:49 ratio of class 0 to class 1, a naïve model with constant probability of 0.49 will yield log-loss score of 0.693, which is regarded as the baseline score for that dataset.\"\n\n\"Higher the imbalance in a dataset, lower the baseline log-loss score of the dataset, due to lower proportion of observations that have higher impact on average of log-loss values.\"\n\nhttps://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a\n\n\n#Log loss aka Cross entropy\n\nBinary Cross Entropy aka Log Loss-The cost function used in Logistic Regression\nBy Megha Setia — November 9, 2020\n\nWhat is Log Loss?\n\"Log Loss is the most important classification metric based on probabilities. It’s hard to interpret raw log-loss values, but log-loss is still a good metric for comparing models. For any given problem, a lower log loss value means better predictions. Mathematical interpretation:\"\n\n\"Log Loss is the negative average of the log of corrected predicted probabilities for each instance.\"\n\n\"In short, there are three steps to find Log Loss: To find corrected probabilities; Take a log of corrected probabilities and Take the negative average of the values we get in the 2nd step.\"\n\nhttps://www.analyticsvidhya.com/blog/2020/11/binary-cross-entropy-aka-log-loss-the-cost-function-used-in-logistic-regression/\n\n\n#Football Prediction Performance: How to Calculate Hit-ratio and Log-loss\nBy Octosport.io - Nov 25, 2021\n\nhttps://medium.com/geekculture/football-prediction-performance-how-to-calculate-hit-ratio-and-log-loss-1e5e22310497\n\n\n#I'm lost and still trying to get all that Log Loss 👀😄\n\n\"You will never do it wrong\" Octosport.io words. \n\nI'll never do it wrong? That's because you don't know me. Error is my second name.😏", "votes": 5 }, { "id": 1723152, "postDate": "2022-03-15T07:08:43.020Z", "rawMarkdown": "", "votes": 1, "isDeleted": true, "replies": [ { "id": 1723369, "postDate": "2022-03-15T11:17:39.003Z", "content": "Thank you for your comment rh
", "rawMarkdown": "Thank you for your comment rh" } ] } ], "comments": [ { "id": 1723152, "author_name": "", "author_url": "", "post_date": "2022-03-15T07:08:43.020000", "content": "", "votes": 1, "replies": [ { "id": 1723369, "author_name": "Marília Prata", "author_url": "", "post_date": "2022-03-15T11:17:39.003000", "content": "Thank you for your comment rh
", "votes": 0, "replies": [] } ] } ], "raw_markdown_by_id": { "1721229": "#Log Loss and Multinomial LR\n\n\"Log loss is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true. The log loss is only defined for two or more labels.\"\n\nhttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html\n\n#Log Loss and Multiclass\nBy Max Kuhn\n\n\"Log loss is a measure of the performance of a classification model. A perfect model has a log loss of 0.\"\n\n\"Compared with accuracy(), log loss takes into account the uncertainty in the prediction and gives a more detailed view into the actual performance. For example, given two input probabilities of .6 and .9 where both are classified as predicting a positive value, say, \"Yes\", the accuracy metric would interpret them as having the same value. If the true output is \"Yes\", log loss penalizes .6 because it is \"less sure\" of it's result compared to the probability of .9.\"\n\nMulticlass\n\n\"Log loss has a known multiclass extension, and is simply the sum of the log loss values for each class prediction. Because of this, no averaging types are supported.\"\n\nhttps://yardstick.tidymodels.org/reference/mn_log_loss.html\n\n\n#How to calculate Log Loss\n\nIntuition behind Log-loss score - By Gaurav Dembla - Nov 17, 2020\n\n\"Where i is the given observation/record, y is the actual/true value, p is the prediction probability, and ln refers to the natural logarithm (logarithmic value using base of e) of a number.\"\n\n\"Log-loss value is calculated for each observation based on observation’s actual value (y) and prediction probability (p). In order to evaluate a model and summarize its skill, log-loss score of the classification model is reported as average of log-losses of all the observations/predictions.\"\n\n\"A model with perfect skill has a log-loss score of 0. In other words, the model predicts each observation’s probability as the actual value. \"\n\n\"What log-loss score is to a classification problem, mean squared error (MSE) is to a regression problem. Both the metrics indicate how good or bad the prediction results are by denoting how far the predictions are from the actual values. \"\n\n\"A model with lower log-loss score is better than the one with higher log-loss score, provided both the models are applied to the same distribution of dataset. We cannot compare log-loss scores of two models applied on two different datasets.\"\n\n\"Baseline log-loss score for a dataset is determined from the naïve classification model, which simply pegs all the observations with a constant probability equal to % of data with class 1 observations. For a balanced dataset with a 51:49 ratio of class 0 to class 1, a naïve model with constant probability of 0.49 will yield log-loss score of 0.693, which is regarded as the baseline score for that dataset.\"\n\n\"Higher the imbalance in a dataset, lower the baseline log-loss score of the dataset, due to lower proportion of observations that have higher impact on average of log-loss values.\"\n\nhttps://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a\n\n\n#Log loss aka Cross entropy\n\nBinary Cross Entropy aka Log Loss-The cost function used in Logistic Regression\nBy Megha Setia — November 9, 2020\n\nWhat is Log Loss?\n\"Log Loss is the most important classification metric based on probabilities. It’s hard to interpret raw log-loss values, but log-loss is still a good metric for comparing models. For any given problem, a lower log loss value means better predictions. Mathematical interpretation:\"\n\n\"Log Loss is the negative average of the log of corrected predicted probabilities for each instance.\"\n\n\"In short, there are three steps to find Log Loss: To find corrected probabilities; Take a log of corrected probabilities and Take the negative average of the values we get in the 2nd step.\"\n\nhttps://www.analyticsvidhya.com/blog/2020/11/binary-cross-entropy-aka-log-loss-the-cost-function-used-in-logistic-regression/\n\n\n#Football Prediction Performance: How to Calculate Hit-ratio and Log-loss\nBy Octosport.io - Nov 25, 2021\n\nhttps://medium.com/geekculture/football-prediction-performance-how-to-calculate-hit-ratio-and-log-loss-1e5e22310497\n\n\n#I'm lost and still trying to get all that Log Loss 👀😄\n\n\"You will never do it wrong\" Octosport.io words. \n\nI'll never do it wrong? That's because you don't know me. Error is my second name.😏", "1723152": "" } }