# Machine Learning Classification – 8 Algorithms for Data Science Aspirants

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In this article, we will look at some of the important machine learning classification algorithms.

We will discuss the various algorithms based on how they can take the **data**, that is, classification algorithms that **can** take **large input data** and those algorithms that **cannot** take large input information.

### Machine Learning Classification Algorithms

Classification is one of the most important aspects of **supervised learning**.

In this article, we will discuss the various classification algorithms like **logistic regression**, **naive bayes**, **decision trees**, **random forests** and many more. We will go through each of the algorithm’s classification properties and how they work.

#### 1. Logistic Regression Algorithm

Logistic regression may be a **supervised learning** classification algorithm wont to **predict the probability** of a target variable. It’s one among the only ML algorithms which will be used for various classification problems like **spam detection**, **Diabetes prediction**, **cancer detection** etc.

Logistic regression is simpler to **implement**, **interpret**, and really **efficient** to coach.

If the amount of observations is lesser than the **amount of features**, **Logistic Regression** shouldn’t be used, otherwise, it’s going to cause **overfitting**.

We use logistic regression for the binary classification of **data-points**. We perform categorical classification such that an output belongs to either of the **two classes (1 or 0)**.

**F****or example –** we can predict whether it will rain today or not, based on the **current weather conditions**.

Two of the important parts of logistic regression are** Hypothesis and Sigmoid Curve**. With the help of this hypothesis, we can derive the **likelihood** of the event.

The data generated from this hypothesis can fit into the **log function** that creates an** S-shaped curv**e known as** “sigmoid”**. Using this **log function**, we can further predict the **category of class**.

We can represent the sigmoid as follows:

The produced graph is through this logistic function:

**1 / (1 + e^-x)**

The** ‘e’** in the above equation represents the **S-shaped curve** that has values between** 0 and 1**.

We write the equation for **logistic regression** as follows:

**y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))**

In the above equation, **b0** and **b1** are the **two coefficients** of the **input x**.

We estimate these two coefficients using **“maximum likelihood estimation”**.

#### 2. Naïve Bayes Algorithm

Naïve Bayes algorithm may be a supervised learning algorithm, which is predicated on **Bayes theorem** and used for **solving classification problems**. It’s not one algorithm but a **family of algorithms** where all of them share a standard principle, i.e. every pair of features being classified is **independent** of every other.

Naïve Bayes Classifier is one among the **straightforward** and **best Classification** algorithms which helps in building the **fast machine learning** models which will make **quick predictions**.

Naive Bayes is one of the **powerful machine learning** algorithms that is used for classification. It is an **extension** of the Bayes theorem wherein each feature assumes **independence**. It is used for a variety of tasks such as **spam filtering** and other areas of **text classification**.

**Naive Bayes algorithm is useful for:**

- It is an
**easy**and**quick way**to**predict**the**class**of the**dataset.**Using this, one can perform a**multi-class prediction**. - When the assumption of
**independence**is**valid,**Naive Bayes is much more**capable**than the other algorithms like**logistic regression**. - Furthermore, you will require
**less training data**.

**Naive Bayes however, suffers from the following drawbacks:**

- If the
**categorical variable**belongs to a category that wasn’t followed up in the**training set**, then the model will give it a probability of**0**which will**inhibit**it from**making any prediction**. - Naive Bayes assumes independence between its
**features**. In real life, it is difficult to gather data that involves completely**independent features**.

It still has some shortcomings though. If the categorical variable falls in any of the categories that the model was not trained on then it will assign this feature a probability of zero which will limit the ability of the model to make predictions . Furthermore, Naive Bayes work under the premise of features independence, which is rarely true in the real world datasets.

#### 3. Decision Tree Algorithm

Decision Tree algorithms are used for **both predictions** as well as **classification** in machine learning.

Using the decision tree with a given **set of inputs**, one can **map** the **various outcomes** that are a result of the **consequences** or **decisions**.

We can understand decision trees with the following **example**:

Let us assume that you have to go to the market to buy some products. At first, you will assess if you really need the product.

Suppose, you will only buy shampoo if you run out of it. If you do not have the shampoo, you will evaluate the weather outside and see if it is raining or not. If it is not raining, you will go and otherwise, you will not.

We can **visualize** this in the form of a decision tree as follows:

This decision tree is a result of various **hierarchical steps** that will help you to reach certain decisions. In order to build this tree, there are two steps – **Induction** and **Pruning**. In induction, we **build a tree** whereas, in pruning, we **remove** the **several complexities** of the tree.

Decision Trees are very flexible because they work with continuous and nominal variables. They are also easy to depict graphically, which makes them valuable for analyzing the decision-making processes. However, they can be computationally very intensive and could easily over fit when used with large data sets. These concerns are trimmed by techniques such as pruning and other ensemble methods inclusive of Random Forest.

#### 4. K-Nearest Neighbors Algorithm

K-nearest neighbors is one of the most **basic** yet **important** classification algorithms in machine learning.

KNNs belong to the **supervised learning domain** and have several applications in **pattern recognition**, **data mining**, and **intrusion detection**. These KNNs are used in real-life scenarios where **non-parametric** algorithms are required. These algorithms do not make any assumptions about how the **data** is **distributed**.

When we are given prior data, the KNN classifies the coordinates into groups that are identified by a **specific attribute**.

#### 5. Support Vector Machine Algorithm

Support Vector Machines are a type of supervised machine learning algorithm that provides **analysis of data** for **classification** and **regression** **analysis**.

While they can be used for **regression**, **SVM** is mostly used for **classification**. We carry out plotting in the **n-dimensional space**. The value of each feature is also the value of the **specified coordinate**. Then, we find the ideal **hyperplane** that differentiates between the two classes.

These support vectors are the **coordinate representations** of **individual observation**. It is a **frontier method** for **segregating** the **two classes**.

#### 6. Random Forest Algorithm

Random Forest classifiers are a **type of ensemble** learning method that is used for **classification**, **regression** and **other tasks** that can be performed with the help of the **decision trees**. These decision trees can be **constructed** at the **training time** and the **output** of the class can be either **classification** or **regression.**

With the help of these random forests, one can correct the habit of **overfitting** to the training set.

Some of the advantages and disadvantages of random forest classifiers are as follows:

**Advantages –** Random Forest Classifiers facilitate the **reduction** in the **over-fitting** of the **model** and these classifiers are more **accurate** than the **decision trees** in several cases.

**Disadvantages –** Random forests exhibit** real-time prediction** but that is **slow** in **nature.** They are also difficult to **implement** and have a **complex algorithm**.

#### 7. Stochastic Gradient Descent Algorithm

Stochastic Gradient Descent (SGD) is a class of **machine learning algorithms** that is apt for **large-scale learning**. It is an efficient approach towards **discriminative learning** of **linear classifiers** under the **convex loss function** which is **linear** **(SVM)** and **logistic regression**.

We apply SGD to the large scale machine learning problems that are present in **text classification** and other areas of **Natural Language Processing**. It can **efficiently scale** to the problems that have more than **10^5 training** examples provided with more than** 10^5 features**.

Following are the advantages of Stochastic Gradient Descent:

- These algorithms are
**efficient**. - We can
**implement**these algorithms quite easily.

However, **Stochastic Gradient Descent (SGD)** suffers from the following disadvantages:

- The SGD algorithm requires a number of
**hyperparameters**such has regularization and a**number of iterations**. - It is also quite
**sensitive**to**feature scaling**, which is one of the most important steps under data**-preprocessing**.

#### 8. Kernel Approximation Algorithm

In this **submodule**, there are various functions that perform an **approximation** of the feature maps that correspond to certain kernels which are used as examples in the **support vector machines**. These feature functions perform a **wide array** of **non-linear transformations** of the input which serves as the **basis of linear** classifications or the other algorithms.

An advantage of using the approximate features that are also **explicit** in nature compared with the kernel trick is that the **explicit mappings** are better at **online learning** that can significantly **reduce the cost** of learning on **very large datasets**.

The **standard kernelized SVMs** cannot scale properly to the large datasets but with an approximate kernel map, one can utilize many efficient **linear SVMs**.

### Summary

In the above article, we learned about the various algorithms that are used for **machine learning classification**. These algorithms are used for a **variety of tasks** in classification. We also analyzed their **benefits** and **limitations.**

The aim of this blog was to provide a clear picture of each of the **classification algorithms** in machine learning.

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