## How do you use expectation maximization?

The expectation-maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables. It does this by first estimating the values for the latent variables, then optimizing the model, then repeating these two steps until convergence.

**What is the EM algorithm in R?**

The EM algorithm can be seen an unsupervised clustering method based on mixture models. It follows an iterative approach, sub-optimal, which tries to find the parameters of the probability distribution that has the maximum likelihood of its attributes in the presence of missing/latent data.

**What is Expectation Maximization EM clustering?**

Expectation Maximization (EM) is a classic algorithm developed in the 60s and 70s with diverse applications. It can be used as an unsupervised clustering algorithm and extends to NLP applications like Latent Dirichlet Allocation¹, the Baum–Welch algorithm for Hidden Markov Models, and medical imaging.

### What is expectation maximization?

The Expectation-Maximization algorithm aims to use the available observed data of the dataset to estimate the missing data of the latent variables and then using that data to update the values of the parameters in the maximization step.

**Does K mean expectation maximization?**

Process of K-Means is something like assigning each observation to a cluster and process of EM(Expectation Maximization) is finding likelihood of an observation belonging to a cluster(probability). This is where both of these processes differ.

**How does EM algorithm work?**

It works by choosing random values for the missing data points, and using those guesses to estimate a second set of data. The new values are used to create a better guess for the first set, and the process continues until the algorithm converges on a fixed point.

#### Why do we need Expectation Maximization algorithm?

EM is used because it’s often infeasible or impossible to directly calculate the parameters of a model that maximizes the probability of a dataset given that model.

**Which is better Kmeans or EM?**

The results showed that the processing speed was slower than that with the EM clustering, but the classification accuracy of the data was 94.7467% (Table 2), which is 7.3171% better than that obtained by EM. Naturally, the inaccuracy of the K-means was lower as compared to that of the EM algorithm.

**What is expectation maximization for missing data?**

Expectation maximization is applicable whenever the data are missing completely at random or missing at random-but unsuitable when the data are not missing at random. To illustrate, consider the following extract of data. Conceivably, individuals who do not answer questions about depression tend to be very depressed.

## What is the difference between k-means and EM?

EM and K-means are similar in the sense that they allow model refining of an iterative process to find the best congestion. However, the K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; and EM uses statistical methods.

**What is the difference between k-means and Expectation Maximization?**

Process of K-Means is something like assigning each observation to a cluster and process of EM(Expectation Maximization) is finding likelihood of an observation belonging to a cluster(probability).

**Why is k-means Expectation Maximization?**

Expectation maximization EM optimizes the marginal likelihood of the data (likelihood with hidden variables summed out). Like K-means, it’s iterative, alternating two steps, E and M, which correspond to estimating hidden variables given the model and then estimating the model given the hidden variable estimates.

### Why KNN is called lazy learner?

Why is the k-nearest neighbors algorithm called “lazy”? Because it does no training at all when you supply the training data. At training time, all it is doing is storing the complete data set but it does not do any calculations at this point.

**What is expectation maximization in imputation?**

It uses the E-M Algorithm, which stands for Expectation-Maximization. It is an iterative procedure in which it uses other variables to impute a value (Expectation), then checks whether that is the value most likely (Maximization). If not, it re-imputes a more likely value.

**How do you handle missing data in a dataset?**

Imputing the Missing Value

- Replacing With Arbitrary Value.
- Replacing With Mode.
- Replacing With Median.
- Replacing with previous value – Forward fill.
- Replacing with next value – Backward fill.
- Interpolation.
- Impute the Most Frequent Value.