I believe that this answer is more correct than the other answers here: from sklearn.tree import _tree def tree_to_code(tree, feature_names): tree_ = tree.tree_ feature_name = [ feature_names[i] if i != _tree.TREE_UNDEFINED else “undefined!” for i in tree_.feature ] print “def tree({}):”.format(“, “.join(feature_names)) def recurse(node, depth): indent = ” ” * depth if tree_.feature[node] != _tree.TREE_UNDEFINED: … Read more
Approach 1: You can use pandas’ pd.get_dummies. Example 1: import pandas as pd s = pd.Series(list(‘abca’)) pd.get_dummies(s) Out[]: a b c 0 1.0 0.0 0.0 1 0.0 1.0 0.0 2 0.0 0.0 1.0 3 1.0 0.0 0.0 Example 2: The following will transform a given column into one hot. Use prefix to have multiple dummies. … Read more
I think that biases are almost always helpful. In effect, a bias value allows you to shift the activation function to the left or right, which may be critical for successful learning. It might help to look at a simple example. Consider this 1-input, 1-output network that has no bias: The output of the network … Read more
Numpy solution. We will shuffle the whole dataset first (df.sample(frac=1, random_state=42)) and then split our data set into the following parts: 60% – train set, 20% – validation set, 20% – test set In [305]: train, validate, test = \ np.split(df.sample(frac=1, random_state=42), [int(.6*len(df)), int(.8*len(df))]) In [306]: train Out[306]: A B C D E 0 0.046919 … Read more
There are at least two separate issues with your question. The first one should be clear by now from the comments by Dr. Snoopy and the other answer: accuracy is meaningless in a regression problem, such as yours; see also the comment by patyork in this Keras thread. For good or bad, the fact is … Read more
Yes, you can apply the ML model but before that understanding of your problem statement come into a picture with all of the feature name available in the data set. If you are having big dataset try to convert it into a cluster of 2 or else take a small dataset to analyze what your … Read more
Looking at your comments, an appropriate solution would be: A = imread(‘Car.jpg’); % loads the image with a crop tool, select the region to be cropped B = imcrop(A); close % Displays the cropped region figure, imshow(B);
The output is the result of automatic evaluation via cross-validation. Of course it is not a prediction, because it has not been applied to new data yet. But you were not asked to execute a prediction anyway.
Probably your best bet is to use ImageNet. It’s a huge dataset that contains a lot of images and label categories. You can find a list of the categories contained and their corresponding codes here. Palm trees have the code n12582231, so you can use this link to access the URLs of the images which … Read more
There are a lot of resources available, books, journals, sources, programs. But let me suggest very simple algorithm. 1. Train/Save HMM acoustic model for each speaker. If u have 10 speakers, than u have ten models. Now ask a current speaker to say magic words like ‘Ok google’. Now recognize it against every models saved. … Read more