Learn the important concepts of machine learning and its key role in data science.

Become a Data Scientist

In this module, we'll learn about the basics of machine learning and how it plays a key role in data science for predictive analysis.
We'll learn types of machine learning and a few important methods such as feature selection, wrapper, and intrinsic method. We'll go through classification problems, unsupervised learning, and big data tools as well. 
By the end of this module, we'll uncover more details behind important machine learning concepts.

An Introductory Guide to Machine Learning



## Dummy Regressors

In Machine Learning, **we start working on the problem, by developing a baseline model** and then improving upon the model using other models or other Feature Selection or Engineering Techniques. There is a famous saying 

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**All models are wrong, but some are useful.**

</center>

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Scikit Learn provides us with `DummyRegressor`, which involves using simple rules to make predictions and developing a baseline model for the regression problem at hand. It is not recommended for real time problems. The `DummyRegressor` class takes in a parameter `str`, which stands for strategy. This parameter decides which technique to use for making predictions. It can take one of the following values: 


* **"Mean"**: Always predicts the mean of the training targets.

* **"Median"**: Always predicts the median of the training targets.

* **"Quantile"**: always predicts a user-provided quantile of the training targets.

* **"Constant"**: always predicts a constant value that is provided by the user.

In all these strategies, the model's predict method completely ignores the input data.






# Dummy Regressors

In Machine Learning, **we start working on the problem, by developing a baseline model** and then improving upon the model using other models or other Feature Selection or Engineering Techniques. There is a famous saying 

---


<center> 

**All models are wrong, but some are useful.**

</center>

---

Scikit Learn provides us with `DummyRegressor`, which involves using simple rules to make predictions and developing a baseline model for the regression problem at hand. It is not recommended for real time problems. The `DummyRegressor` class takes in a parameter `str`, which stands for strategy. This parameter decides which technique to use for making predictions. It can take one of the following values: 


* **"Mean"**: Always predicts the mean of the training targets.

* **"Median"**: Always predicts the median of the training targets.

* **"Quantile"**: always predicts a user-provided quantile of the training targets.

* **"Constant"**: always predicts a constant value that is provided by the user.

In all these strategies, the model's predict method completely ignores the input data.




Learn about Dummy Regressors, which enable us to build baseline Regression models.  

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Conclusion

Dummy Regressors

Dummy Regressors