Logistic regression is used for classification problems because it predicts the probability that a given input point belongs to a certain class. The core idea is to find a relationship between features and the probability of particular outcomes. Unlike linear regression which outputs a continuous number, logistic regression transforms its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes.

Logistic Regression is often associated with classification problems because its primary purpose is to model the probability of a binary outcome. The logistic regression model predicts the probability that an observation belongs to a particular category (class 1) based on one or more independent variables. Despite having "regression" in its name, logistic regression is used for classification, not regression.

Assignment: Q1. Why is logistic regression a classification problem? Logistic regression is a statistical method used for binary classification problems, meaning problems where the outcome variable (or dependent variable) has two possible classes or categories. It's called "regression" because it involves the logistic function, but it's used for classification rather than regression tasks. Logistic Function (Sigmoid): Probability Interpretation: Decision Boundary: Maximum Likelihood Estimation: Linear Decision Boundary: and I have done this video with 100% practice.

Assignment: Q1. Why is logistic regression a classification problem? Logistic regression is a statistical method used for binary classification problems, meaning problems where the outcome variable (or dependent variable) has two possible classes or categories. It's called "regression" because it involves the logistic function, but it's used for classification rather than regression tasks. Logistic Function (Sigmoid): Probability Interpretation: Decision Boundary: Maximum Likelihood Estimation: Linear Decision Boundary:

Logistic Regression is the key to successfully classifying the feature values based on probability.

Assignment of Day-75, 15-dec-2023 ML (day-8)Why is logistic regression a classification problem?Logistic regression is a classification algorithm because it predicts the probability of an observation belonging to a specific class rather than predicting a continuous value as in regression problems. The logistic function and the thresholding process make it suitable for binary classification tasks, where the goal is to assign observations to one of two classes based on their feature values.

AOA, I learned in this lecture how the sigmoid function works in LOGISTIC REGRESSION in Python. STEPS OF A LOGISTIC REGRESSION: 1-Import Libraries 2-Load the data set [ df = sns.load_dataset('titanic' ) ] 3-Pre process the data 4-Remove the deck column 5-Impute missing values in age and fare 6-Impute missing values in embark and embarked town 7-Encode the categorical variables using for loop where object and categoy data types are given 8-Split the data in X and y column 9-Split the data in train and test 10-Call the model 11-Train the model 12-Predict 13-Evaluate the model 14-Plot the model of confusion matrix 15-Save the model 16-Load the modelALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey, Ameen.