handson-ml
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ageron
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
Ran this code for the first exercise - ``` from sklearn.model_selection import GridSearchCV param_grid = [{'weights': ["uniform", "distance"], 'n_neighbors': [3, 4, 5]}] knn_clf = KNeighborsClassifier() grid_search = GridSearchCV(knn_clf, param_grid, cv=5,...
Hello I would like to know how I can create a workspace directory on Windows. I have installed Python 3.8 and have been trying to put the following function on...
Hello, Could you please help in telling me the below issue when I am trying to import the regression model and train it on the dataset in Chapter 2. **INPUT**...
Hi there, in the following code, I fail to understand how the hash function is working here and how would it make sure that the test set is randomly selected....
I am using random forest model to predict the sales quantity. I have listed all parts in a row based while the quantity is in a monlthy based (each column...
In the line: `skfolds = StratifiedKFold(n_splits=3, random_state=42)` we need to implement `shuffle=True` parameter since the default of `shuffle` is set to `False`. Just something small, but hope it helps.
Dear Agron I am following ur work which is in `extra_capsnets.ipynb`. You use MNIST data set for your work. My question is how I can use my own dataset and...
In Transformation Pipeline make class DataFrameSelector for custom transformation and call DataFrameSelector(num_attribs) it show TypeError: object() takes no parameters and same with CombinedAttributesAdder i m using colab ``` from sklearn.base...
Hi Aurélien, Thank you for a GREAT book! I really enjoy reading it and practicing the examples in the book. In chapter 2, when we need to transform the test...
Why use StrafiedShuffleSplit, instead of train_test_split with attribute ```stratify```? ```X_train, X_test = train_test_split(housing.values, test_size=0.2, stratify=housing['income_cat'], random_state=42)``` I think its more clear and pythonic that make folds and use a ```for```...
