Fixing this ** “typeerror fit missing 1 required positional argument y”** error message is very simple and easy.

In this article, we will help you to troubleshoot this error, aside from that, we’ll discuss important details.

That includes, what this error means, why does it occur, and the solutions that will get rid of this error message.

**What is “typeerror fit missing 1 required positional argument y”?**

The ** “fit missing 1 required positional argument y”** is a common error message that can occur in machine learning and data science algorithms in Python.

The error message indicates that a required argument “y” is missing when calling the “fit” method on a machine learning model or function.

In addition to that,** **this error message usually raised when you forget to instantiate a class before calling its method.

For instance, when you are using a class from the ** sklearn library **such as GaussianNB, you need to instantiate it before calling its fit method.

Instead of writing model = GaussianNB, you should write model = GaussianNB().

**Why does this error occur?**

The error message can occur due to various reasons, such as:

- Incorrect input data
- Missing or incorrect arguments in the code
- Outdated libraries
- The dimensions of the ‘X’ and ‘y’ arrays are not compatible.

**How to fix “typeerror fit missing 1 required positional argument y”?**

To fix this error message, you need to ensure that the “y” variable is provided as an input to the machine learning model’s “fit” method.

The following are the different solutions you may use:

**1. Check the data type of y**

Make sure that the ‘y’ parameter is a numpy array or pandas series, not a list or other data type.

**from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np
# Load the data into a Pandas data frame
df = pd.read_csv('data.csv')
# Split the data into features (X) and target (y)
X = df[['feature1', 'feature2']]
y = np.array(df['target']) # Convert y to a numpy array
# Create a Linear Regression model
model = LinearRegression()
# Fit the model to the data
model.fit(X, y)**

**2: Check the dimensions of X and y**

Make sure that ‘X’ is a 2D array (or pandas dataframe) and ‘y’ is a 1D array (or pandas series).

**from sklearn.linear_model import LinearRegression
import pandas as pd
# Load the data into a Pandas data frame
df = pd.read_csv('data.csv')
# Split the data into features (X) and target (y)
X = df[['feature1', 'feature2']]
y = df['target']
# Check the dimensions of X and y
print('X shape: ', X.shape)
print('y shape: ', y.shape)
# Create a Linear Regression model
model = LinearRegression()
# Fit the model to the data
model.fit(X, y)
**

This should fix the error and allow you to train the model without any issues.

**3: Ensure the ‘y’ parameter is passed to the fit() method**

The error message indicates that the ‘y’ parameter is missing from the fit() method. That’s you have to ensure that the ‘y’ parameter is included in the fit() method.

**from sklearn.linear_model import LinearRegression
import pandas as pd
# Load the data into a Pandas data frame
df = pd.read_csv('data.csv')
# Split the data into features (X) and target (y)
X = df[['feature1', 'feature2']]
y = df['target']
# Create a Linear Regression model
model = LinearRegression()
# Fit the model to the data
model.fit(X=X, y=y)**

**Conclusion**

By executing all the effective solutions for the ** “typeerror fit missing 1 required positional argument y”** that this article has already provided above, it will help you resolve the error.

We are hoping that this article provides you with sufficient solutions.

You could also check out other “**typeerror**” articles that may help you in the future if you encounter them.

- Typeerror object of type is not json serializable
- Typeerror cannot read property ‘map’ of undefined
- Typeerror cannot set properties of undefined

Thank you very much for reading to the end of this article.