What are the three types of error in a #ML model?
👉 1. Bias - error caused by choosing an algorithm that cannot accurately model the signal in the data, i.e. the model is too general or was incorrectly selected. For example, selecting a simple linear regression to model highly non-linear data would result in error due to bias.
👉 2. Variance - error from an estimator being too specific and learning relationships that are specific to the training set but do not generalize to new samples well. Variance can come from fitting too closely to noise in the data, and models with high variance are extremely sensitive to changing inputs. Example: Creating a decision tree that splits the training set until every leaf node only contains 1 sample.
👉 3. Irreducible error - error caused by noise in the data that cannot be removed through modeling. Example: inaccuracy in data collection causes irreducible error.
❇️ @AI_Python_EN
👉 1. Bias - error caused by choosing an algorithm that cannot accurately model the signal in the data, i.e. the model is too general or was incorrectly selected. For example, selecting a simple linear regression to model highly non-linear data would result in error due to bias.
👉 2. Variance - error from an estimator being too specific and learning relationships that are specific to the training set but do not generalize to new samples well. Variance can come from fitting too closely to noise in the data, and models with high variance are extremely sensitive to changing inputs. Example: Creating a decision tree that splits the training set until every leaf node only contains 1 sample.
👉 3. Irreducible error - error caused by noise in the data that cannot be removed through modeling. Example: inaccuracy in data collection causes irreducible error.
❇️ @AI_Python_EN
Decision trees are extremely fast when it comes to classify unknown records. Watch this video to know how Decision Tree algorithm works, in an easy way - http://bit.ly/2Ggsb9l
#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop
#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop
ANNOUNCING PYCARET 1.0.0 - An amazingly simple, fast and efficient way to do machine learning in Python. NEW OPEN SOURCE ML LIBRARY If you are a DATA SCIENTIST or want to become one, then this is for YOU....
PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.
It allows you to go from preparing data to deploying a model within SECONDS.
PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:
- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!
Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.
The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.
To learn more about PyCaret, please visit the official website https://www.pycaret.org
#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret
❇️ @AI_Python_EN
PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.
It allows you to go from preparing data to deploying a model within SECONDS.
PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:
- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!
Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.
The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.
To learn more about PyCaret, please visit the official website https://www.pycaret.org
#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret
❇️ @AI_Python_EN