Classification
Last updated
Last updated
It is easy to say that the linear regression predicts a “value” of the targeted variable through a linear combination of the given features, while on the other hand, a Logistic regression predicts “probability value” through a linear combination of the given features plugged inside a logistic function. Linear regression is unbounded, and this brings logistic regression into picture. Their value strictly ranges from 0 to 1.
One more thing to note here is that logistic regression uses maximum likelihood estimation (MLE) instead of least squares method of minimizing the error which is used in linear models. In Linear regression we minimized SSE. In Logistic Regression we maximize log likelihood instead. Linear regression uses mean squared error as its cost function. If this is used for logistic regression, then it will be a non-convex function of parameters (theta). Gradient descent will converge into global minimum only if the function is convex.
In classification problems, various evaluation metrics are used to assess the performance of a machine learning model. The choice of metric depends on the specific characteristics of your problem and your priorities, such as the relative importance of false positives and false negatives. Here's an explanation of common classification metrics and when to use them, along with examples:
Accuracy:
Use Case: Suitable for balanced datasets where false positives and false negatives have similar consequences.
Example: In a spam email classifier, where both false positives (legitimate emails marked as spam) and false negatives (spam emails in the inbox) are undesirable.
Precision:
Use Case: When minimizing false positives is crucial, and you want to ensure that the positive predictions made by your model are highly accurate.
Example: Medical diagnoses like cancer detection, where false positives can lead to unnecessary treatments and stress.
Recall (Sensitivity or True Positive Rate):
Use Case: When minimizing false negatives is critical, and you want to ensure that your model captures as many positive instances as possible.
Example: An airport security system for detecting prohibited items, where missing a threat (false negative) is far more serious than a false alarm.
F1 Score:
Use Case: Balances precision and recall, suitable when you want a single metric that considers both false positives and false negatives.
Example: Information retrieval systems, where you need to find relevant documents (recall) while minimizing the number of irrelevant ones (precision).
Specificity (True Negative Rate):
Use Case: Relevant in scenarios where minimizing false positives is essential, like fraud detection.
Example: Credit card fraud detection, where it's important to correctly identify non-fraudulent transactions (true negatives) to prevent blocking legitimate transactions.
ROC AUC (Receiver Operating Characteristic Area Under the Curve):
Use Case: Useful when comparing different models or assessing the overall performance of a classifier across different thresholds.
Example: Evaluating the performance of various machine learning algorithms in a credit scoring task.
Matthews Correlation Coefficient (MCC):
Use Case: Appropriate for imbalanced datasets, where there is a significant difference in class frequencies.
Example: Anomaly detection in network security, where normal events far outnumber anomalous ones.
F-beta Score:
Use Case: Allows you to adjust the balance between precision and recall using the parameter beta.
Example: When you want to prioritize either precision (beta < 1) or recall (beta > 1) depending on the specific needs of your application.
Remember that the choice of metric should be based on the specific goals and trade-offs of your problem. It's often a good practice to consider multiple metrics, especially when the consequences of false positives and false negatives differ significantly in your application.
It seems like there might be a typo in your question. I assume you are referring to the Naive Bayes algorithm. Naive Bayes is a classification algorithm, not "naive bias." Let me provide a detailed explanation of the Naive Bayes algorithm.
Naive Bayes is a probabilistic machine learning algorithm used for classification tasks, such as spam email detection, sentiment analysis, and text categorization. It is based on Bayes' theorem, which calculates the probability of an event based on prior knowledge of conditions related to that event.
The Naive Bayes algorithm makes a simplifying assumption known as the "naive" assumption, which is that all features used in the classification are conditionally independent of each other given the class label. This means that the presence or absence of one feature does not affect the presence or absence of another feature.
3. Model Representation: In Naive Bayes, the goal is to calculate the probability of a particular class (C) given a set of features (X₁, X₂, ..., Xᵢ). This is represented as:
Where:
P(C | X₁, X₂, ..., Xᵢ) is the posterior probability of class C given the features X₁ through Xᵢ.
P(C) is the prior probability of class C.
P(Xᵢ | C) is the conditional probability of feature Xᵢ given class C.
Training: To train a Naive Bayes classifier, you need labeled training data where you know both the features and the corresponding class labels. The training process involves:
a. Calculating Prior Probabilities (P(C)):
Calculate the prior probability of each class, i.e., the probability that an example belongs to that class based on the training data.
b. Estimating Conditional Probabilities (P(Xᵢ | C)):
For each feature Xᵢ and each class C, estimate the conditional probability that the feature Xᵢ occurs given the class C. This is typically done using techniques like Maximum Likelihood Estimation (MLE) or Laplace smoothing (to handle zero probabilities).
Types of Naive Bayes: There are different variants of Naive Bayes classifiers, including:
Gaussian Naive Bayes: Assumes that continuous features follow a Gaussian distribution.
Multinomial Naive Bayes: Used for discrete data like text data, where features represent word counts or frequencies.
Bernoulli Naive Bayes: Suitable for binary data, where features are binary variables.
Advantages:
Naive Bayes is simple, computationally efficient, and scales well to high-dimensional data.
It works well with small to moderate-sized datasets.
It is particularly effective for text classification tasks like spam detection and sentiment analysis.
Limitations:
The "naive" assumption of feature independence may not hold in some real-world scenarios.
It can perform poorly when features are highly correlated.
Handling of continuous and numerical data may require additional preprocessing.
Despite its simplifying assumptions, Naive Bayes is a powerful and often surprisingly effective algorithm, especially for text classification tasks and situations where feature independence is a reasonable approximation.
Let's explain this using an example. The table below shows the main outputs from the logistic regression. It is very obvious which are the categorial variables out here:
