Select training attributes and target
Select X,y for Machine Learning model training. The X
matrix is used as model input, whereas y
vector is used as model target.
Required packages
You need below packages to use the code generated by recipe. All packages are automatically installed in MLJAR Studio.
pandas>=1.0.0
Interactive recipe
You can use below interactive recipe to generate code. This recipe is available in MLJAR Studio.
In the below recipe, we assume that you have following variables available in your notebook:
- df_1 (type DataFrame)
- df_2 (type DataFrame)
Python code
# Python code will be here
Example Python notebooks
Please find inspiration in example notebooks
- Train Random Forest regressor
The `scikit-learn` provides implementation of [Random Forest](/glossary/random-forest/) ...
- Visualize Decision Tree
The Decision Tree algorithm's structure is human-readable, a key advantage. In ...
- Decision Tree features importance
`Scikit-learn's` permutation importance assesses the impact of each feature on ...
- Train Decision Tree classifier
Classification is a task of predicting discrete target labels. The Python `scikit-learn` ...
- Train Decision Tree on Iris data set
Python is a great choice for Machine Learning projects, because of rich ML packages ...
- Train Decision Tree regressor
Train a Decision Tree Regressor using scikit-learn. This machine learning algorithm ...
- Train Random Forest classifier
Python implementation of Random Forest algorithm available in `scikit-learn` package ...
- Save and load Decision Tree
`Scikit-learn` provides Decision Tree algorithms for classification (`DecisionTreeClassifier`) ...
- Tune Decision Tree classifier
This notebook demonstrates tuning a Decision Tree model. We'll find the best hyperparameters ...
- MLJAR AutoML solution for Kaggle Playground series s4e6
Kaggle is a platform for data science competitions. It has Playground series of ...
Data wrangling cookbook
Code recipes from Data wrangling cookbook.
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- Select Columns