Search results
May 6, 2023 · 1. If you want to map coefficient names to their values you can use. def logreg_to_dict(clf: LogisticRegression, feature_names: list[str]) -> dict[str, float]: coefs = np.concatenate([clf.intercept_, clf.coef_.squeeze()]) return dict(zip(["intercept"] + feature_names, coefs)) feature_names is a list of features the model was trained on.
I have a binary prediction model trained by logistic regression algorithm. I want know which features (predictors) are more important for the decision of positive or negative class. I know there is coef_ parameter which comes from the scikit-learn package, but I don't know whether it is enough for the importance.
Jan 13, 2017 · 1 scikit-learn: sklearn.linear_model.LogisticRegression. sklearn.linear_model.LogisticRegression from scikit-learn is probably the best: as @TomDLT said, Lasso is for the least squares (regression) case, not logistic (classification). from sklearn.linear_model import LogisticRegression. model = LogisticRegression(.
Aug 14, 2020 · 0. I am able to print the p-values of my regression but I would like my output to have the X2 value as the key and the p-value next to it. I want the output to look like this: attr1_1: 3.73178531e-01. sinc1_1: 4.97942222e-06. the code: from sklearn.linear_model import LogisticRegression. from scipy import stats.
Jun 10, 2021 · The equation of the tangent line L(x) is: L(x)=f(a)+f′(a)(x−a). Take a look at the following graph of a function and its tangent line: From this graph we can see that near x=a, the tangent line and the function have nearly the same graph. On occasion, we will use the tangent line, L(x), as an approximation to the function, f(x), near x=a.
Nov 22, 2017 · Accuracy is one of the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations. Higher accuracy means model is preforming better. Accuracy = TP+TN/TP+FP+FN+TN. TP = True positives. TN = True negatives.
May 13, 2021 · Logistic Regression is an optimization problem that minimizes a cost function. Regularization adds a penalty term to this cost function, so essentially it changes the objective function and the problem becomes different from the one without a penalty term.
@George Apologies for not being clear. I just want to ensure that the parameters I pass into my Logistic Regression are the best possible ones. I would like to be able to run through a set of steps which would ultimately allow me say that my Logistic Regression classifier is running as well as it possibly can. –
Aug 23, 2017 · There's an argument in the method for considering only the interactions. So, you can write something like: poly = PolynomialFeatures(interaction_only=True,include_bias = False) poly.fit_transform(X) Now only your interaction terms are considered and higher degrees are omitted. Your new feature space becomes [x1,x2,x3,x1*x2,x1*x3,x2*x3] You can ...
24. I'm solving a classification problem with sklearn's logistic regression in python. My problem is a general/generic one. I have a dataset with two classes/result (positive/negative or 1/0), but the set is highly unbalanced. There are ~5% positives and ~95% negatives. I know there are a number of ways to deal with an unbalanced problem like ...
