1. What is Geremi?
Geremi is a Python-based machine learning framework that can help developers quickly build, train and deploy machine learning models. Geremi provides a variety of machine learning algorithms, including classification, regression, clustering, dimensionality reduction and so on. In addition, Geremi also provides some convenient tools, such as data preprocessing and model evaluation.
Second, how to install Geremi?
Installing Geremi is very simple, just enter the following command in the terminal:
```
pipinstallgremlin
```
Third, how to use Geremi?
1. data preprocessing
Data preprocessing is a very important step in machine learning. Geremi provides various data preprocessing tools, such as data cleaning, feature selection and feature scaling. The following is a simple example of data preprocessing:
``` python skin
from gremlin . preprocessingimportstandardscaler
from gremlin . datasetsimportload _ iris
data=load_iris()
X = data ['data']
Y = data ['target']
scaler=StandardScaler()
X=scaler.fit_transform(X)
```
In the code above, we use StandardScaler to scale data features.
2. Model training
After data preprocessing, we can start training the model. Geremi provides a variety of machine learning algorithms, such as linear regression, logical regression, decision tree and so on. The following is a simple model training example:
``` python skin
from gremlin . linear _ modelimportLinearRegression
from gremlin . datasetsimportload _ Boston
data=load_boston()
X = data ['data']
Y = data ['target']
Model = Linear Regression ()
model.fit(X，y)
```
In the above code, we use LinearRegression to train a linear regression model.
3. Model evaluation
After model training, we need to evaluate the model. Geremi provides various evaluation tools, such as cross-validation, ROC curve, accuracy, recall and so on. The following is a simple model evaluation example:
``` python skin
from gremlin . metrics importaccuracy _ score
from gremlin . datasetsimportload _ iris
from gremlin . linear _ modelimportLogisticRegression
data=load_iris()
X = data ['data']
Y = data ['target']
model=LogisticRegression()
model.fit(X，y)
y_pred=model.predict(X)
Accuracy = accuracy _ score (y, y _ prediction)
Print ("Accuracy:", Accuracy)
```
In the code above, we use accuracy_score to calculate the accuracy of the model.