Contents
When use logistic regression over SVM?
SVM try to maximize the margin between the closest support vectors whereas logistic regression maximize the posterior class probability. SVM is deterministic (but we can use Platts model for probability score) while LR is probabilistic. For the kernel space, SVM is faster.
Why logistic regression perform better than neural network?
A neural network is more complex than logistic regression. In practice, a neural network model for binary classification can be worse than a logistic regression model because neural networks are more difficult to train and are more prone to overfitting than logistic regression.
Can logistic regression outperform SVM?
Difference between SVM and Logistic Regression SVM works well with unstructured and semi-structured data like text and images while logistic regression works with already identified independent variables. The risk of overfitting is less in SVM, while Logistic regression is vulnerable to overfitting.
Which is better logistic regression or Perceptron?
In that case of course the difference is that the logistic regression uses a logistic function and the perceptron uses a step function. In general both algorithm should yield the same decision boundary (at least for a single neuron perceptron).
What are the advantages and disadvantages of logistic regression?
Advantages Logistic Regression is one of the simplest machine learning algorithms and is easy to implement yet provides great training efficiency in some cases. Also due to these reasons, training a model with this algorithm doesn’t require high computation power.
When to use regularization techniques in logistic regression?
This usually happens in the case when the model is trained on little training data with lots of features. So on high dimensional datasets, Regularization techniques should be considered to avoid over-fitting (but this makes the model complex).
Can a non linear problem be solved with logistic regression?
Non-linear problems can’t be solved with logistic regression because it has a linear decision surface. Linearly separable data is rarely found in real-world scenarios. Good accuracy for many simple data sets and it performs well when the dataset is linearly separable.
When to use logistic regression for overfitting?
If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space. It constructs linear boundaries.