logistic-regression

How the probabilities are normalized in one-vs-rest scheme of sklearn Logistic Regression? Question: In the sklearn LogisticRegression classifer, we can set the muti_class option to ovr which stands for one-vs-rest, as in the following code snippet: # logistic regression for multi-class classification using built-in one-vs-rest from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression # define …

My code giving differnt result where as the same code in my Machine learning assignment expects a different result? Question: MY CODE def lrCostFunction(theta, X, y, lambda_): m = y.size if y.dtype == bool: y = y.astype(int) tempt = theta tempt[0] = 0 J = 0 grad = np.zeros(theta.shape) hx = X.dot(theta.T) h = sigmoid(hx) …

sklearn logistic regression loss value during training Question: Is there a way to obtain loss value at each iteration while training a logistic regression? Python sklearn show loss values during training has an working example for SGDRegressor however not working for logistic regression. Asked By: haneulkim || Source Answers: I think you should change the …

Use SHAP values to explain LogisticRegression Classification Question: I am trying to do some bad case analysis on my product categorization model using SHAP. My data looks something like this: corpus_train, corpus_test, y_train, y_test = train_test_split(data[‘Name_Description’], data[‘Category_Target’], test_size = 0.2, random_state=8) vectorizer = TfidfVectorizer(stop_words=’english’, ngram_range=(1, 3), min_df=3, analyzer=’word’) X_train = vectorizer.fit_transform(corpus_train) X_test = vectorizer.transform(corpus_test) model …

One Hot Encoding giving nan values in python Question: I have a classification case study where I am using Logistic Regression model. I want to use One Hot Encoding to convert my categorical column (SalStat) values into 0 and 1. This is my code: data2["SalStat"] = data2["SalStat"].map({"less than or equal to 50,000":0, "greater than 50,000":1}) …

TypeError: 'numpy.float64' object is not callable – While Printing F1 Score Question: I am trying to run below code on Jupyter Notebook: lr = LogisticRegression(class_weight=’balanced’) lr.fit(X_train,y_train) y_pred = lr.predict(X_train) acc_log = round(lr.score(X_train, y_train) * 100, 2) prec_log = round(precision_score(y_train,y_pred) * 100,2) recall_log = round(recall_score(y_train,y_pred) * 100,2) f1_log = round(f1_score(y_train,y_pred) * 100,2) roc_auc_log = roc_auc_score(y_train,y_pred) When …

Why does LogisticRegressionCV's .score() differ from cross_val_score? Question: I was using LogisticRegressionCV’s .score() method to yield an accuracy score for my model. I also used cross_val_score to yield an accuracy score with the same cv split (skf), expecting the same score to show up. But alas, they were different and I’m confused. I first did …

ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT Question: I have a dataset consisting of both numeric and categorical data and I want to predict adverse outcomes for patients based on their medical characteristics. I defined a prediction pipeline for my dataset like so: X = dataset.drop(columns=[‘target’]) y = dataset[‘target’] …

Logistic regression: X has 667 features per sample; expecting 74869 Question: Using a imdb movie reviews dataset i have made a logistic regression to predict the sentiment of the review. tfidf = TfidfVectorizer(strip_accents=None, lowercase=False, preprocessor=None, tokenizer=fill, use_idf=True, norm=’l2′, smooth_idf=True) y = df.sentiment.values X = tfidf.fit_transform(df.review) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, test_size=0.3, shuffle=False) …

Setting exact number of iterations for Logistic regression in python Question: I’m creating a model to perform Logistic regression on a dataset using Python. This is my code: from sklearn import linear_model my_classifier2=linear_model.LogisticRegression(solver=’lbfgs’,max_iter=10000) Now, according to Sklearn doc page, max_iter is maximum number of iterations taken for the solvers to converge. How do I specifically …