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# How to do SVD with Dask?

# How to do SVD with Dask?

This recipe helps you do SVD with Dask

How to do SVD with Dask?

SVD stands for Singular value Decomposition. This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD).

```
import dask.array as da
import dask
from dask_ml.decomposition import TruncatedSVD
import dask.array as da
```

```
svd = TruncatedSVD(n_components=5, n_iter=3, random_state=42)
svd.fit(X)
```

```
print(svd.explained_variance_ratio_.sum())
```

Visualizing how this tranformer works.

```
X = da.random.random((100, 10), chunks=(50, 10)).persist()
x, y, z = da.linalg.svd(X)
dask.visualize(x, y, z)
```

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