#### Model vs Algorithm in ML

Machine Learning works with “models” and “algorithms”, and both play an important role in machine learning where the algorithm tells about the process, and the model is built by following those rules. But in most cases, data science beginners get confused regarding the aspects of **model vs algorithm in ML**. So let’s have a look at how models and algorithms differ from each other.

While the majority of data science aspirants rush to learn the maximum number of the algorithm, at the same time, they ignore the learning aspects of modelling.

I have heard so many data science students saying, ‘Why waste time learning ML models? rather we can invest the same in strengthening our base in the statistical approach of the algorithm.’

The key reason behind such a learning approach is the myth that tells modelling can be learnt while doing the practical project. And it’s true but only to a partial extent. Rather, unlike the algorithm, you need to hold a strong conceptual base on ML modelling too.

Let’s have an elaborated approach to these two important terms of data science. This will help you understand the approach of model vs algorithm in ML. But, first, let’s start with the short definition of both.

## So, What can be the brief definition of the model?

A machine learning model is a function used to take some certain input, perform a certain operation that is told by algorithms to its best on the given input, and give a suitable output.

## How will you define the algorithm?

**Algorithms** have been derived by the statistician or mathematician very long ago, and those algorithms are studied and applied by individuals for their business purposes.

Some of the machine learning algorithms are:

- Linear regression
- Logistic regression
- Decision tree
- Random forest
- K-nearest neighbour
- K-means learning

Now keep the above short definition aside for a few minutes and start entering into the deeper prospect of model vs algorithm in ML. To have a clear idea of model vs algorithm, let’s first know the definition of both model and algorithm from machine learning.

Let’s head to the ML Algorithms first.

**What is an algorithm in Machine learning?**

An algorithm is a step by step approach powered by statistics that guide machine learning in its learning process. An algorithm is nothing but one of the several components that constitute a model.

There are several characteristics of machine learning algorithms:

- First, machine learning algorithms can be represented by the use of mathematics and pseudo code.
- The effectiveness of machine learning algorithms can be measured and represented.
- With any of the popular programming languages, the implementation of machine learning algorithms is possible.

**What is the Model in Machine learning?**

The model is dependent on factors such as features selection, tuning parameters, cost functions. However, along with the algorithm, the model just is not fully dependent on algorithms.

Model results from an algorithm when we implement the algorithm with the code when we train the algorithms with the real data. A model tells what your program learned from the data by following the rules of those algorithms. The model is used to predict the future result observed by the algorithm implementation of small data.

** Model = Data + Algorithm **

A model contains four major steps that are:

**How do models and algorithms work together in machine learning? Understand the model Vs algorithm in a better way.**

For example:

y = mx+c is an equation for a line where m is the slope of the line and c is the y-intercept. This is nothing but linear regression with only one variable.

Similarly, the decision tree and random forest have something like the Gini index and K-nearest having Euclidean distance formula.

So take the linear regression algorithm:

- Start with a training set with x1, x2,…, and y.
- Find out the parameters c0, c1, c2 with the random variables.
- Find out the learning rate alpha.
- Then repeat the following updates such as c0 = co-alpha +h(x)-y and for c1, c2 also.
- Repeat these processes till they converge.

When you employ this algorithm, you employ these exact five steps in your model without changing the steps, your model initiated by the algorithm, and the same dataset.

If you want to apply that algorithm to the model, the model finds out the value of m and c that we don’t know, then how will you find out?

Suppose you have three variables that are having values of x and y. Now your model will find the value of m1, m2, m3, and c1, c2, c3 for three variables.

The model will work with three slopes and three intercepts to determine the result of the dataset to predict the future.

**The “algorithm”** might be treating all the data the same, but it is the “**model”** that solves the problems. Thus, an algorithm is something that you use to train the model on the data.

After building a model, data science enthusiasts test it to get the accuracy of that model and fine-tuning it to improve the results.

## Why should adequate attention be given to learning the modelling process?

Working with models can be best practised while doing the real-time project. But, you should be well aware of the right time and the right way to implement your modelling approach within the above mentioned four parts. Until you master the basics of the modelling approach, your designed model may lack efficacy.

### How will you assess the efficacy of your model?

Well. The following measures will help you to determine the efficiency level of a machine learning model.

**Performance of your machine learning algorithm towards identified performance**

You have a performance goal for your machine learning algorithm. As soon as you deploy your machine learning model, check if your algorithm is giving the expected result or not. Negative deviation from the result expected from a particular algorithm may be due to ineffective model designing.

**Percentage of algorithm coverage**

This point might sound a bit tricky, but even you can’t deny that some data scientists think the higher the number of used ML algorithms the better will be the model performances.

In reality, the efficacy criteria are the opposite.

A highly efficient ML model should always provide the best performance by incorporating the least possible number of statistical algorithms.

Unnecessary use of a larger number of the algorithm makes the analysis process complex and a time consuming one.

**Better Scopes for Data tuning in a future project**

First, let me what is the key goal of a smart machine learning engineer?

Designing such a machine learning system will prove a good solution for upcoming business issues by altering the models and algorithms at a minimal possible percentage. This is your answer, right?

So, while developing an ML model, ensure that it offers ample opportunities for data tuning according to the situational changes in the data sets.

For the upcoming projects, for uninterrupted performance, regular up-gradation of the current model becomes essential. From this perspective, the deployed model has over 60% of performance enhancement by a simple tuning of 20% of current algorithms.

This article may help you to understand the algorithm and model in Machine learning, In summary, an algorithm is a process or a technique that we follow to get the result or to find the solution to a problem. And a model is a computation or a formula formed as an output of an algorithm that takes some input, so you can say that you are building a model using a given algorithm.

This amount of concept is not enough for your machine learning Studies. To grab lucrative machine learning opportunities, you need to learn in dept.

## Where can you learn machine learning?

A huge number of **free online data science courses** and reference machine learning books are available across the internet. For self-paced learning, you can choose one. But if you have a goal to secure a future proof machine learning engineering designation by the end of this year, you need to target learning the concepts.

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