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Machine Learning FAQ

It may help to get a better grasp of our problem by plotting “learning curves” For example, here I plotted the average accuracies of a model (using 10-fold cross validation).

The blue line (training accuracy) shows the average accuracy on the training folds and the green line shows the average accuracy on the test fold for different sizes of the initial training set.

Train/Test Split and Cross Validation in Python

I’ll explain what that is — when we’re using a statistical model (like linear regression, for example), we usually fit the model on a training set in order to make predications on a data that wasn’t trained (general data).

As mentioned, in statistics and machine learning we usually split our data into to subsets: training data and testing data (and sometimes to three: train, validate and test), and fit our model on the train data, in order to make predictions on the test data.

We don’t want any of these things to happen, because they affect the predictability of our model — we might be using a model that has lower accuracy and/or is ungeneralized (meaning you can’t generalize your predictions on other data).

Let’s see what under and overfitting actually mean: Overfitting means that model we trained has trained “too well” and is now, well, fit too closely to the training dataset.

Basically, when this happens, the model learns or describes the “noise” in the training data instead of the actual relationships between variables in the data.

In contrast to overfitting, when a model is underfitted, it means that the model does not fit the training data and therefore misses the trends in the data.

The training set contains a known output and the model learns on this data in order to be generalized to other data later on.

Let’s load in the diabetes dataset, turn it into a data frame and define the columns’ names: Now we can use the train_test_split function in order to make the split.

Now we’ll fit the model on the training data: As you can see, we’re fitting the model on the training data and trying to predict the test data.

Here is a summary of what I did: I’ve loaded in the data, split it into a training and testing sets, fitted a regression model to the training data, made predictions based on this data and tested the predictions on the test data.

What if one subset of our data has only people from a certain state, employees with a certain income level but not other income levels, only women or only people at a certain age?

Here is a very simple example from the Sklearn documentation for K-Folds: And let’s see the result — the folds: As you can see, the function split the original data into different subsets of the data.

Because we would get a big number of training sets (equals to the number of samples), this method is very computationally expensive and should be used on small datasets.

Display Deep Learning Model Training History in Keras

Hi Jason, I wrote a LSTM model to train my brain MRI slices.

For my dataset, each patient has 50 slices, and n patients are divided into training and validation sets .

model.add(LSTM(128, input_shape = (max_timesteps, num_clusters), activation=’tanh’, recurrent_activation=’elu’, return_sequences = False, stateful = False, name=’lstm_layer’))

model.compile(loss = ‘categorical_crossentropy’, optimizer = optimizer, metrics=[‘accuracy’])

model.fit(X_train, y_train, validation_data=(X_vald, y_vald), epochs = epoch_num, batch_size = batch_size, shuffle = True) First, I use the GlobalAveragePooling layer of fine-tuned GoogLeNet to extract the feature of each slice. Second,

the n1*50*2048 features from training set and n2*50*2048 features from validation set are used to train my LSTM model. However,

2.5 Evaluating forecast accuracy

Let $y_{i}$ denote the $i$th observation and $\hat{y}_{i}$ denote

The forecast error is simply $e_{i}=y_{i}-\hat{y}_{i}$, which is on

used scale-dependent measures are based on the absolute errors or

on a single data set, the MAE is popular as it is easy to understand

The percentage error is given by $p_{i} = 100 e_{i}/y_{i}$. Percentage

frequently used to compare forecast performance between different data

It is included here only because it is widely used, although we will

to using percentage errors when comparing forecast accuracy across

time series, a useful way to define a scaled error uses naïve

of the original data, $q_{j}$ is independent of the scale of the

a scaled error can be defined using seasonal naïve forecasts: [

forecast is recommended when using time series data.) The mean absolute

Figure 2.17: Forecasts of Australian quarterly beer production using data up to the end of 2005.

Figure 2.17 shows three forecast methods applied to the quarterly Australian

beer production using data only to the end of 2005.

It is obvious from the graph that the seasonal naïve method is best for these

results point to the seasonal naïve method as the best of these three

Here, the best method is the drift method (regardless of which accuracy measure

It is important to evaluate forecast accuracy using genuine forecasts. That

well a model performs on new data that were not used when fitting the

data for fitting, and use the rest of the data for testing the model,

to measure how well the model is likely to forecast on new data.

The size of the test set is typically about 20% of the total sample, although

this value depends on how long the sample is and how far ahead you

Some references describe the test set as the 'hold-out set' because these

call the training set the 'in-sample data' and the test set

the training set consists only of observations that occurred prior to

a reliable forecast based on a very small training set, so the earliest

This procedure is sometimes known as a 'rolling forecasting origin' because

With time series forecasting, one-step forecasts may not

procedure based on a rolling forecasting origin can be modified

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