Mean Squared Error Logistic Regression
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Root Mean Square Error Interpretation
hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask r brier score Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. linear regression model diagnostics Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Measuring accuracy of a logistic regression-based model
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up vote 14 down vote favorite 7 I have a trained logistic regression model that I am applying to a testing data set. The dependent variable is binary (boolean). For each sample in the testing data set, I apply the logistic regression model to generates a % probability that the dependent variable will be true. Then I record whether the acutal value was true or false. I'm trying to calculate an $R^2$ or Adjusted $R^2$ figure as in a
Logistic Regression Sklearn
linear regression model. This gives me a record for each sample in the testing set like: prob_value_is_true acutal_value .34 0 .45 1 .11 0 .84 0 .... .... I am wondering how to test the accuracy of the model. My first attempt was to use a contingency table and say "if prob_value_is_true > 0.80, guess that the actual value is true" and then measure the ratio of correct to incorrect classifications. But I don't like that, because it feels more like I'm just evaluating the 0.80 as a boundary, not the accuracy of the model as a whole and at all prob_value_is_true values. Then I tried to just look at each prob_value_is_true discrete value, as an example, looking at all samples where prob_value_is_true=0.34 and measuring the % of those samples where the acutal value is true (in this case, perfect accuracy would be if the % of samples that was true = 34%). I might create a model accuracy score by summing the difference at each discrete value of prob_value_is_true. But sample sizes are a huge concern here, especially for the extremes (nearing 0% or 100%), such that the averages of the acutal values are not accurate, so using them to measure the model accuracy doesn't seem right. I even tried creating huge ranges to ensure sufficient sample sizes (0-.25, .25-.50, .50-.75, .75-1.0), but how to measure "goodness" of that % of actual v
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss calculate rmse in r the workings and policies of this site About Us Learn more about
Brier Score Interpretation
Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions confusion matrix Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. http://stats.stackexchange.com/questions/18178/measuring-accuracy-of-a-logistic-regression-based-model Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top How can root mean square error be used to predict logistic regression model accuracy? up vote 0 down vote favorite The problem which I have at hand consists http://stats.stackexchange.com/questions/105403/how-can-root-mean-square-error-be-used-to-predict-logistic-regression-model-accu of a logistic regression model for risk evaluation that has been made on some credit card data of Quarter-1'12 (Jan'12 - Mar'12). Now I use the same model to evaluate risk for the data of Quarter-2'12(Apr'12-Jun'12). I want to devise an accuracy score that gives an insight into how "accurately" my model fits the new data (Quarter -2 data). I have used Hosmer Lemeshow Statistic and Balanced Accuracy Method till now but none served the purpose. What further can be done in this regard? regression logistic accuracy share|improve this question asked Jul 1 '14 at 12:39 Kasha2592 11 The mean square error in the predict probability for binary outcome case is the Brier score, which is a proper scoring rule. Optimizing proper scoring rules corresponds to finding predicted probabilities that are well calibrated to the actual probabilities in the data. Is this the kind of "accuracy" you are looking for? –Matt Jul 1 '14 at 14:10 @Matthew I am looking for a measure that can ensure logistic regression model (made on
Consulting Quick Question Consultations Hourly Statistical Consulting Results Section Review Statistical Project Services Free Webinars Webinar Recordings Contact Customer Login Statistically Speaking Login Workshop Center Login All Logins Assessing the Fit of Regression Models by Karen A http://www.theanalysisfactor.com/assessing-the-fit-of-regression-models/ well-fitting regression model results in predicted values close to the observed data values. The mean model, which uses the mean for every predicted value, generally would be used if there were no informative predictor variables. The fit of a proposed regression model should therefore be better than the fit of the mean model. Three statistics are used in Ordinary Least Squares (OLS) regression to evaluate model logistic regression fit: R-squared, the overall F-test, and the Root Mean Square Error (RMSE). All three are based on two sums of squares: Sum of Squares Total (SST) and Sum of Squares Error (SSE). SST measures how far the data are from the mean and SSE measures how far the data are from the model's predicted values. Different combinations of these two values provide different information about how the mean squared error regression model compares to the mean model. R-squared and Adjusted R-squared The difference between SST and SSE is the improvement in prediction from the regression model, compared to the mean model. Dividing that difference by SST gives R-squared. It is the proportional improvement in prediction from the regression model, compared to the mean model. It indicates the goodness of fit of the model. R-squared has the useful property that its scale is intuitive: it ranges from zero to one, with zero indicating that the proposed model does not improve prediction over the mean model and one indicating perfect prediction. Improvement in the regression model results in proportional increases in R-squared. One pitfall of R-squared is that it can only increase as predictors are added to the regression model. This increase is artificial when predictors are not actually improving the model's fit. To remedy this, a related statistic, Adjusted R-squared, incorporates the model's degrees of freedom. Adjusted R-squared will decrease as predictors are added if the increase in model fit does not make up for the loss of degrees of freedom. Likewise, it will increase as predictors are added if the increase in model fit is worthwhile. Adjusted R-squared s